Metabolomic Signatures for Predicting, Diagnosing, and Prognosing Various Diseases Including Cancer

ABSTRACT

A system and method for using new biomarkers to assess individual diseases is provided. In one embodiment of the present invention, absolute quantification of annotated metabolites by mass spectrometry is used to identify certain biomarkers and derivatives thereof (i.e., signatures), which are then used to screen for, diagnose, predict, prognose, and treat various diseases, including, but not limited to, breast cancer, ovarian cancer, colorectal cancer, pancreatic cancer, and acute graft-versus-host disease.

RELATED APPLICATIONS DATA

This application claims priority to several provisional patentapplications, including Ser. No. 62/685,275, which was filed on Jun. 14,2018, Ser. No. 62/714,650, which was filed on Aug. 3, 2018, Ser. No.62/830,389, which was filed on Apr. 6, 2019, and Ser. No. 62/838,683,which was filed on Apr. 25, 2019, the subject matter of which areincorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to new biomarkers for assessing variousdiseases, and in particular to the use of absolute quantification ofannotated metabolites by mass spectrometry to identify certainbiomarkers and derivatives thereof (i.e., signatures) that can be usedto screen for, diagnose, predict, prognose, and treat various diseases,including, but not limited to breast cancer, ovarian cancer, colorectalcancer, pancreatic cancer, and acute graft-versus-host disease, to namea few.

2. Description of Related Art

MYC is a member of a family of regulator genes and proto-oncogenes thatcode for transcription factors. As such, MYC leads to the increasedexpression of many genes, some of which are involved in metabolicreprogramming and cell proliferation, contributing to the formation ofcancer. In fact, it is largely accepted that in order to meet cancerbiochemical requirements tumor metabolism become addicted to local MYConcogene activation. However, studies performed by the inventors suggestthat in situ tumor gene activation should be seen as a confinedreplication of a previously existent systemic inborn-like conditionalready detectable in cancer-free participants at elevated risk ofcancer development.

The latter is the effect of variable elevated levels of insulinresistance over patients exhibiting phenotypic mild deficiencies knownas Fatty Acids Oxidation Defects (FAOD) exhibiting energy productiondeficiencies due to β-oxidation impairments followed by hypoglycemia dueto insufficiencies in gluconeogenesis pathways.

Indeed, in these patients, high insulin levels systemically activate MYCproto-oncogene inducing glutaminolysis, glycolysis, Δ9-stearoyl-CoAdesaturase (SCD) activity and inhibition of liver gluconeogenesis. Whenadded to prominent blood levels of very-long chain acylcarnitines,lactate, fumarate and succinate, the final phenotypic scenario is highlysuggestive of peroxisome and/or mitochondrial β-oxidation dysfunctions.

As an example, in studies conducted by the inventors, the phenotypicquantification of this MYC-induced “ambiance” was able to accuratelydiscriminate between breast cancer patients from controls at AUC=0.994(95% CI:0.978-1), Sensitivity=98.72%, Specificity=98.26%, PPV=98.09%,NPV=98.83%, Average Accuracy=0.982 (100-fold cross validations) andPredictive Accuracy Statistics p<9.2e-06 (1000 permutations)irrespective to disease stage, histology, intrinsic subtypeclassification, BMI, menopausal status, age, and patient's continentalgeographic localization (South American or European Continent).

As a proof-of-principle of the direct connections to stemness andcancer, the phenotypic metabolic deviations identified in the studieswere highly correlated to human embryo metabolism and exhibited elevatedpredictive capabilities of chemotherapy response and outcomes ofsurvival. The validation process of these findings, besides confirmationin independent cohorts, were also present, to a considerable extend, inother malignancies of glandular origin.

This research provides biochemical support to the hypothesis of canceras a physical epiphenomenon of a preexisting MYC-induced systemiccondition. In addition to ratifying local malignant lipidogenesis,glutaminolysis and glycolysis as major drivers in cancer, this study isone of the first to provide largely validated biochemical support to thehypothesis of cancer as a physical epiphenomenon of a systemic,preexistent, stemmness-like MYC-related condition, that according toresults of the studies, closely resemble specific inborn errors ofmetabolism.

In doing so, the inventors relied on targeted quantitative metabolomics,which is the absolute quantitative measurement, by liquid chromatographyfollowed by tanden mass spectrometry (LC-MS/MS), of low molecular weightcompounds covering key biochemically active metabolites belonging to thewhole range of pathways related to biosynthesis, signaling andcatabolism of (i) structural and non-structural lipids, (ii) aminoacids, (iii) biogenic amines, and (iv) components of intermediarymetabolisms.

Considered as the gold standard of quantification, the very recentpopularity of clinical mass spectrometry can be attributed to the highspecificity, accuracy and reliability due to the direct analysis of ionsthat constitute that specific analyte, without the risk of crossreactivity as described for direct antibody assay detection.

The capability to analyze large arrays of annotated metabolites extractsbiochemical information reflecting true functional end-points of overtbiological events while genomics, transcriptomics and proteomicstechnologies, though highly valuable, merely indicate the potentialcause for phenotypic response, and therefore cannot necessarily predictdrug effects, toxicological response or disease states at the phenotypelevel unless functional validation is added. Metabolomics bridges thisinformation gap by depicting functional information, since metabolitedifferences in biological fluids and tissues provide the closest link tothe various phenotypic responses.

Needless to say, such changes in the biochemical phenotype are of directinterest to pharmaceutical, biotech and health industries onceappropriate technology allows the cost-efficient mining and integrationof this information. In general, phenotype is not necessarily predictedby genotype. The gap between genotype and phenotype is spanned by manybiochemical reactions each with individual dependencies to variousinfluences, including drugs, nutrition and environmental factors.

In this chain of biomolecules from the genes to phenotype, metabolitesare the quantifiable molecules with the closest link to phenotype.Studies conducted by the inventors show that many phenotypic andgenotypic states, such as a toxic response to a drug or diseaseprevalence are predicted by differences in the concentrations offunctionally relevant metabolites within biological fluids and tissue.

Thus, in light of the foregoing, it would be advantageous to develop asystem and method that uses targeted metabolomics, or absolutequantification of annotated metabolites by mass spectrometry, toidentify certain biomarkers and derivatives thereof, such as ratios,etc. (i.e., “signatures”) that can be used to screen for, diagnose,predict, prognose, and treat various diseases.

SUMMARY OF THE INVENTION

The present invention provides a system and method for using newbiomarkers to assess individual diseases. Preferred embodiments of thepresent invention include use of absolute quantification of annotatedmetabolites by mass spectrometry to identify certain biomarkers andderivatives thereof (i.e., “signatures”), which can then be used toscreen for, diagnose, predict, prognose, and treat various diseases.

In one embodiment of the present invention, targeted metabolomicanalysis of plasma and/or tissue samples are performed. Absolutequantification (μmol/L) of blood metabolites is achieved by targetedquantitative profiling of certain (e.g., up to 186) annotatedmetabolites by electrospray ionization (ESI) tandem mass spectrometry(MS/MS).

In one embodiment of the present invention, a targeted profiling schemeis used to quantitatively screen for fully annotated metabolites usingmultiple reaction monitoring, neutral loss and precursor ion scans.Quantification of metabolite concentrations is performed, resulting inat least one file that includes (i) sample identification, (ii)metabolite names (e.g., up to 186), and (iii) concentrations (e.g.,μmol/L of plasma).

For metabolomic data analysis, log-transformation is then applied to allquantified metabolites to normalize the concentration distributions andprovided to software for comparing (e.g., mapping, plotting, etc.) topreviously known “signatures.” In one embodiment, signatureidentification may involve uploading the data into MetaboAnalyst 3.0 (aweb-based analytical pipeline) and ROCCET (a Receiver OperatingCharacteristic Curve Explorer & Tester) for the generation of uni andmultivariate ROC (Receiver Operating Characteristic) curves obtainedthrough SVM (Support Vector Machine), PLS_DA (Partial LeastSquares-Discriminant Analysis), and Random Forests as well as LogisticRegression Models.

In certain embodiments of the present invention, there are up to 186annotated metabolites that are quantified for comparision, including 40acylcanitines (ACs), 21 amino acids (AAs), 19 biogenic amines (BA), sumof hexoses (Hex), 76 phosphatidylcholines (PCs), 14lyso-phosphatidylcholines (LPCs) and 15 sphingomyelins (SMs).Glycerophospholipids were further differentiated with respect to thepresence of ester (a) and ether (e) bonds in the glycerol moiety, wheretwo letters denote that two glycerol positions are bound to a fatty acidresidue (aa=diacyl, ae=acyl-alkyl), while a single letter indicates thepresence of a single fatty acid residue (a=acyl or e=alkyl). Samples mayalso be analyzed for energy metabolism metabolites, including lactate,pyruvate/oxaloacetate, alpha ketoglutarate, fumarate and succinate.

In addition to individual metabolite quantification, groups ofmetabolites related to specific functions were assembled as ratios basedon previous observation that the proportions between metaboliteconcentrations can strengthen the association signal and at the sametime provide new information about possible metabolic pathways. Asdiscussed below, these ratios are (at least in certain embodiments)extremely important aspects of a disease's “signature,” and can, in andof themselves, indicate the presence or likelihood of a particulardisease, the patient's prognosis, and available treatments.

In other embodiment, other groupings were also found to be important,including groups of amino acids (AA) that are computed by summing thelevels of AAs belonging to certain families or chemical structuresdepending on their functions, such as the sum of: 1) essential aminoacids (essential AA); 2) non-essential amino acids (non-essential AA);3) glucogenic (Ala+Gly+Ser) amino acids (Gluc AA); 4) branched-chain(Leu+Ile+Val) amino acids (BCAA); 5) Aromatic (His+Tyr+Trp+Phe) aminoacids (Arom AA); 6) glutaminolytic derivatives (Ala+Asp+Glu); and 7) thesum of total amino acids.

Groups of acylcarnitines (AC), important to evaluate mitochondrialfunction, may also be computed by summing total acylcarnitines (AC),C2+C3, C16+C18, C16+C18:1 and C16-OH+C18:1-OH). Groups of lipids,important to evaluate lipid metabolism, may also be analyzed bysumming: 1) total lysophosphatidylcholines (total LPC); 2) totalacyl-acyl; and 3) total acyl-alkyl phosphatidylcholines (total PC aa andtotal PC ae, respectively); 4) total sphingomielins (total SM); and 5)sum of total (LPC+PC aa+PC ae+SM) lipids (structural lipids).

Proportions among sums of saturated, monounsaturated and polyunsaturatedstructural lipids may also be assembled as proxies to estimate elongasesand desaturases activities towards ether lipids: 1) Desaturase 9 [(PC aeC36:1+PC ae C38:1+PC ae C42:1)/(PC ae C42:0)], Desaturase 6 [(PC aeC44:6+PC ae C44:5+PC ae C42:5+PC ae C40:6+PC ae C40:5+PC ae C38:6+PC aeC38:5+PC ae C36:5)/(PC ae C36:1+PC ae C38:1+PC ae C42:1)].

Clinical indicators of liver metabolism and function may also beobtained by applying either the classical(leucine+isoleucine+valine/(tyrosine+phenylalanine) or variations(Val/Phe, Xleu/Phe) of the Fischer quotient. Clinical indicators ofisovaleric acidemia, tyrosinemia, urea cycle deficiency and disorders ofβ-oxidation may also be calculated by adopting the ratios ofvalerylcarnitine to butyrylcarnitine (C5/C4), tyrosine to serine(Tyr/Ser), glycine to alanine and glutamine (Gly/Ala, Gly/Gln) andlactate to pyruvate (Lac/Pyr), respectively. Proxies for enzyme functionrelated to the diagnosis of very long-chain acyl-CoA dehydrogenase(VLCAD) and type 2 carnitine-palmitoyl transferase (CPT-2) deficienciesmay also be achieved by assembling the ratios (C16+C18:1/C2),(C14:1/C4), (C14:1-OH/C9), (C14/C9), (C14:1/C9) and to the elongation ofvery-long-chain-fatty acids (ELOVL2) (PC aa C40:3/PC aa C42:5). Levelsof methionine sulfoxide (Met-SO) alone or in combination to unmodifiedmethionine (Met-SO/Met) as well as symmetric (SDMA), asymmetric (ADMA)and total dimethylation of argine residues (Total DMA) may then bequantified to gain access to ROS-mediated protein modifications as wellas to systemic arginine methylation status, respectively.

Knowing that liver inhibition of gluconeogenesis is a bona fideinsulin-MYC-dependent biochemical reaction, a shift from normal to lowervalues in the ratio of glucose to glucogenic amino acids (Glucose/Ser,Glucose/Gly and Glucose/Ala) after insulin administration, may beadopted as a measurement of insulin-MYC-related activity.

The same procedure may then be applied to other MYC-responsive enzymesas follows: arginine methyltransferases (ADMA, ADMA/Arg, SDMA, SDMA/Argand total DMA, total DMA/Arg), ornithine decarboxylase (Glu, Glu/Orn,Pro, Pro/Orn, Orn, Orn/Arg, Putrescine, Putrescine/Orn, Spermidine,Spermidine/Putrescine, Spermine and Spermine/Spermidine), alanine aminotransferase (Ala), (Ala/Glu), aspartate aminotransferase (Asp) and(Asp/Glu), glutaminase (Glu), (Gln/Glu), [(Glu+Asp+Ala)/Gln],[(Gln/Glu)/Asp], (Glu/Glucose)/(Ala/Glu) and[(Glu/Gln)/Glucose]/(Ala/Glu).

The latter two ratios are related to the “glutamate pulling effect,”which is defined as the hypoglycemia-induced up-regulation in thedeaminated, rather than transaminated, production of glutamate throughinsulin-MYC-dependent glutamate dehydrogenase (GDH) stimulation ofglutaminolysis with consequent increased amounts of net keto acids toanaplerosis.

The ratios of (Ser/C2, Ser/Gln, Ser/Thr) and of (PC aa C42:0/PC aeC32:3, PC aa C32:2/PC ae C34:2) as proxies for glycolysis-relatedphosphoglycerate dehydrogenase (PHGDH) and glucokinase regulator (GCKR)activities may also be considered. The later inhibits glucokinaseactivity in liver and pancreas and the former catalyses the ratelimiting step of serine biosynthesis.

In parallel, and assuming the ratio values of glutamine to glutamate(Gln/Glu) and to aspartate [(Gln/Glu)/Asp], as proxys for glutaminolyticactivity, the ratios [(Ser/C2)/(Gln/Glu)], [(Ser/C2)/(Gln/Glu)/Asp],[(PC aa C32:2/PC ae C34:2)/(Gln/Glu)] and [(PC aa C32:2/PC aeC34:2)/(Gln/Glu)/Asp] may be assembled as theoretical equations to gainaccess to the balance between glycolysis and glutaminolysis.

It should be appreciated that with respect to the foregoing metabolitesand sets thereof (e.g., summation, ratio, etc.), certain ones may becritical to analyzing a particular disease, whereas others may be lessimportant. Thus, provided below are metabolites and/or sets thereof thatare critical (i.e., most important) to individual signatures. For thesake of brevity, critical aspects of individual signatures forindividual disease will be covered in the appropriate sections below.

A more complete understanding of a system and method for using newbiomarkers to assess individual diseases will be afforded to thoseskilled in the art, as well as a realization of additional advantagesand objects thereof, by a consideration of the following detaileddescription of the preferred embodiment. Reference will be made to theappended sheets of drawings, which will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a method in accordance with one embodiment of thepresent invention as to how a metabolic signature for a disease isidentified and subsequently used to assess a patient's blood sample asto that disease;

FIGS. 2-6 provide a list of analytes, including their abbreviations,that are considered metabolites (or sets thereof) used in certainembodiments of the present invention;

FIGS. 7A and B provide a list of ratios that have been identified asuseful in assessing different types of diseases;

FIG. 8 provides a list of parameters that have been identified as usefulin assessing at least ovarian cancer;

FIG. 9 provides a list of ratios that have been identified as useful inassessing at least ovarian cancer;

FIG. 10 provides likelihood ratios, and interpretations thereof, used bythe inventors during performance of Statistical Univariate Analysis;

FIG. 11 provides characteristics, and remarks concerning the same, usedby the inventors when identifying ideal tumor markers according toSokoll and Chan;

FIGS. 12A and B provide, respectively, a multivariable ROC curveanalysis for ovarian cancer, along with performance characteristics forthe same;

FIG. 13 provides an Ortho-PLSDA Score's plot for ovary cancer patientscompared to healthy participants and other malignant and non-malignantconditions;

FIGS. 14A-D illustrate certain ratios that are useful in determining asurvival rate (prognoses) for ovary cancer patients;

FIG. 15 provides metabolites and mathematical derivatives thereof (e.g.,ratios, etc.) that are used in one embodiment of the present inventionto assess (e.g., diagnose, prognose, etc.) ovarian cancer in a patient;

FIGS. 16A and B provide, respectively, a multivariable ROC curveanalysis for colon cancer, along with performance characteristics forthe same;

FIG. 17 provides an Ortho-PLSDA Score's plot for colon cancer patientscompared to healthy participants and other malignant and non-malignantconditions;

FIGS. 18A-C illustrate certain ratios that are useful in determining asurvival rate (prognoses) for colon cancer patients;

FIG. 19 provides metabolites and mathematical derivatives thereof (e.g.,ratios, etc.) that are used in one embodiment of the present inventionto assess (e.g., diagnose, prognose, etc.) colon cancer in a patient;

FIGS. 20A and B provide, respectively, a multivariable ROC curveanalysis for pancreatic cancer, along with performance characteristicsfor the same;

FIG. 21 provides an Ortho-PLSDA Score's plot for pancreatic cancerpatients compared to healthy participants and other malignant andnon-malignant conditions;

FIGS. 22A and B illustrate certain ratios that are useful in determininga survical rate (prognosis) for pancreatic cancer patients; and

FIG. 23 provides metabolites and mathematical derivatives thereof (e.g.,ratios, etc.) that are used in one embodiment of the present inventionto assess (e.g., diagnose, prognose, etc.) pancreatic cancer in apatient.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Preferred embodiments of the present invention involve use of targetedmetabolomics, or absolute quantification of annotated metabolites bymass spectrometry, to identify never described biomarkers and/orderivatives thereof (e.g., ratios, etc.) (i.e., “signatures”) suitablefor assessing various diseases, including, but not limited to breastcancer, ovarian cancer, colorectal cancer, pancreatic cancer, and acutegraft-versus-host disease, to name a few.

It should be appreciated that while a first disease (e.g., breastcancer) may have a first signature, and a second disease (e.g., ovariancancer) may have a second, different signature, the method used inidentifying each signature is very similar, and in certain instancesidentical. Thus, while different diseases have been discussed indifferent sections below, for the sake of brevity, details concerninghow a signature is identified and subsequently used to assess aparticular disease are equally applicable to other signatures and otherdiseases unless stated otherwise. For example, details concerningabsolute quantification of annotated metabolites by mass spectrometryprovided in the breast cancer section applies equally to the ovariancancer section, as do other details, unless stated otherwise.

It should also be appreciated that a disease may have more than onesignature or portions thereof. For example, a first signature may beused for diagnoses, a second signature (or portion of the firstsignature) may be used for prognoses, etc. It should also be appreciatedthat while a disease may have more than one signature, there may beindividual aspects (e.g., individual metabolites or derivatives thereof)that are common to several signatures, and can therefore provide, in andof themselves, information on diagnosis, prognosis, treatment, etc.Specifics concerning signatures will be discussed in the correspondingsections below.

It should further be appreciated that the present invention is notlimited to any particular disease, and that those skilled in the artwill understand that the methods disclosed herein can be used toidentify signatures for, and assess, other diseases, including those notspecifically mentioned herein. The present invention is also not limitedto use of mass spectrometry, or any particular type of mass spectrometry(e.g., electrospray ionization (ESI) tandom mass spectrometry (MS/MS),etc.), and includes other methods for quantifying metabolites, such aschromatography or spectrometry. That being said, the inventors havefound that there are benefits to using mass spectrometry, and inparticular ESI MS/MS, and the data analysis described herein (e.g.,log-transformation, ROC curves, etc.). As such, the methods described indetail herein are preferred embodiments, and should be treated as such.

Prior to discussing the inventions, including individual signatures, themethods used to identify the same, and assess various diseases, certaindefinitions of term or phrases used herein will first be provided.

Definitions

By employing the biomarkers (or specific sets thereof) and the methodsaccording to the present invention it has become possible to assess adisease (e.g., ovarian cancer, colorectal cancer, etc.) with improvedaccuracy and reliability. It has surprisingly been achieved in thepresent invention to provide biomarkers or biomarker sets by measuringthe amount and/or ratios of certain metabolites in samples, such asblood samples, of subjects that make it possible to screen and diagnosediseases (e.g., ovary cancer, etc.) in an improved manner and at anearly stage of the disease.

In general, a biomarker is a valuable tool due to the possibility todistinguish two or more biological states from one another, working asan indicator of a normal biological process, a pathogenic process or asa reaction to a pharmaceutical intervention.

A metabolite is a low molecular compound (<1 kDa), smaller than mostproteins, DNA and other macromolecules. Small changes in activity ofproteins result in big changes in the biochemical reactions and theirmetabolites (=metabolic biomarker, looking at the body's metabolism),whose concentrations, fluxes and transport mechanisms are sensitive todiseases and drug intervention.

This enables getting an individual profile of physiological andpathophysiological substances, reflecting both genetics andenvironmental factors like nutrition, physical activity, gut microbialand medication. Thus, a metabolic biomarker gives more comprehensiveinformation than for example a protein or hormone, which are biomarkers,but not metabolic biomarkers.

In view thereof, the term “metabolic biomarker” or short “biomarker” asused herein is defined to be a compound suitable as an indicator of thepresence and state of a disease (e.g., cancer) as well as its subtype(e.g., subtype of tumor), being a metabolite or metabolic compoundoccurring during metabolic processes in the mammalian body.

The terms “biomarker” and “metabolic biomarker” are in general usedsynonymously in the context of the present invention and typically referto the amount of a metabolite or to the ratio of two or moremetabolites. Hence, the term metabolic biomarker or biomarker isintended to also comprise ratios (or other mathematical relationships)between two or more metabolites.

The term “amount” typically refers to the concentration of a metabolitein a sample, such as blood sample, and is usually given in micromol/L,but may also be measured in other units typically used in the art, suchas g/L, mg/dL, etc. The term “sum” typically means the sum of molaramount of all metabolites as specified in the respective embodiment.

The term “ratio” typically means the ratio of amounts of metabolites asspecified in the respective embodiment. The metabolic biomarker (set)measured according to the present invention may comprise the classes ofmetabolites (i.e. analytes) of amino acids and biogenic amines,acylcarnitines, hexoses, sphingolipids and glycerophospholipids, aslisted in FIGS. 2-6.

Biogenic amines in FIG. 2 are understood as a group of naturallyoccurring biologically active compounds derived by enzymaticdecarboxylation of the natural amino acids. A biogenic substance is asubstance provided by life processes, and the biogenic amines contain anamine group.

It has surprisingly been found that measuring a set of biomarkerscomprising these classes of metabolites, i.e. measuring the amountand/or ratios of certain indicative metabolites, allows for screeningand diagnosing various diseases (e.g., ovary cancer, etc.) in animproved manner and at an early stage and allows for assessingbiochemical reflection of tumor activity, allowing for the prediction ofa therapeutic response as well as for sub classification among adisease's behavior.

While a modified “signature” can be used, if one metabolite or one classof metabolites as specified for the respective biomarker combination isomitted or if the number thereof is decreased the assessment of thedisease becomes less sensitive and less reliable.

This particularly applies for the early stages of the disease being notreliably detectable according to known methods using known biomarkers atall. In fact, the measurement of the metabolites contained in therespective sets of biomarkers at the same time allows a more accurateand more reliable assessment of a disease, typically with (A) asensitivity of greater than 80%, preferably greater than 90%, and morepreferably greater than 98%, (B) a specificity of greater than 80%,preferably greater than 85%, and more preferably greater than 90%, (C) apositive predictive value (PPV) of greater than 40%, preferably greaterthan 50%, and more preferably greater than 60%, and (D) a negativepredictive value (NPV) of greater than 80%, preferably greater than 90%,and more preferably greater than 98%. Obviously, biomarkers (or setsthereof) that can reach or achieve 100% (or near 100%) sensitivity,specificity, PPV, and/or NPV is desired.

The meanings of the terms “sensitivity”, “specificity”, “positivepredictive value” and “negative predictive value” are typically known inthe art and are defined in the context of the present inventionaccording to the “Predictive Value Theory”, as established by theUniversity of Iowa, USA. In this theory, the diagnostic value of aprocedure is defined by its sensitivity, specificity, predictive valueand efficiency. Description of the formulae are summarized below.

Sensitivity of a test is the percentage of all patients with diseasepresent who have a positive test. (TP/(TP+FN))×100=Sensitivity (%) whereTP=Test Positive; FN=False Negative.

Specificity of a test is the percentage of all patients without diseasewho have a negative test. (TN/(FP+TN))×100=Specificity (%) where TN=TestNegative; FP=False Positive.

Predictive value of a test is a measure (%) of the times that the value(positive or negative) is the true value, i.e. the percent of allpositive tests that are true positives is the Positive Predictive Value((TP/(TP+FP))×100=Predictive Value of a Positive Result (%);((TN/(FN+TN))×100=Predictive Value Negative Result (%))

Likelihood Ratios: The performance of biomarkers can further be assessedby determining the Positive and Negative Likelihood Ratios (LR) usedherein during Statistical Univariate Analysis (see FIG. 10).

Multivariate Data Analysis: Training cases were used for markerdiscovery and to identify any clinical variable that might be associatedwith a disease (e.g., ovarian cancer, colorectal cancer, etc.) bylogistic regression analysis. Quantification of metaboliteconcentrations and quality control assessment was performed with theMetIDQ® software package (BIOCRATES Life Sciences AG, Innsbruck,Austria). Internal standards serve as the reference for the metaboliteconcentration calculations. An xls file was then exported, whichcontained sample names, metabolite names and metabolite concentrationwith the unit of μmol/L of in plasma.

Data was then uploaded into the web-based analytical pipelineMetaboAnalyst 2.0 (www.metaboanalyst.ca) and normalized usingMetaboAnalyst's normalization protocols (Xia et al 2012) for uni andmultivariate analysis, high dimensional feature selection, clusteringand supervised classification, functional enrichment as well asmetabolic pathway analysis.

Data was also imported to ROCCET (ROC Curve Explorer & Tester) availableat http://www.roccet.ca/ROCCET/ for the generation of uni andmultivariate Receiver Operating Characteristic (ROC) curves obtainedthrough Support Vector Machine (SVM), Partial Least Squares-DiscriminantAnalysis (PLS-DA) and Random Forests.

Curves were generated by Monte-Carlo cross validation (MCCV) usingbalanced subsampling where two thirds (⅔) of the samples were used toevaluate the feature importance. Significant features were then used tobuild classification models, which were validated on the ⅓ of thesamples that were left out. The same procedure was repeated multipletimes to calculate the performance and confidence interval of eachmodel.

Up and down regulation: An up-regulation means an increase in theconcentration of a metabolite, e.g. an increase in the rate of at whichthis biochemical reaction occurs due to for example a change inenzymatic activity. For a down-regulation, it's the other way around.

T-test: The t-test is a statistical hypothesis test and the one used isthe one integrated in the MarkerView software and is applied to everyvariable in the table and determines if the mean for each group issignificantly different given the standard deviation and the number ofsamples, e.g. to find out if there is a real difference between themeans (averages) of two different groups.

P-value: The p-value is the probability of obtaining a result at leastas extreme as the one that was actually observed, assuming that the nullhypothesis (the hypothesis of no change or effect) is true. The p-valueis always positive and the smaller the value the lower the probabilitythat it is a change occurrence. A p-value of 0.05 or less rejects thenull hypothesis at the 5% level, which means that only 5% of the timethe change is a chance occurrence. This is the level set in the tablesprovided herein.

Log-fold change: Log-fold change is defined as the difference betweenthe average log transformed concentrations in each condition. This is away of describing how much higher or lower the value is in one groupcompared to another. For example, a log-fold change of 0.3 is“equivalent” to an exp (0.3)=1.34 fold change increase compared to thecontrol (healthier group). Further, a log-fold change of −0.3 is“equivalent” to a exp(−0.3)=0.74=(1/1.34) fold change increase comparedto the control or decrease fold change of 1.34 to the disease. See FIG.11 for ideal tumor marker according to Sokoll and Chan.

Signatures for particular diseases, including the identification thereofand use of the same for assessing (e.g., screening, diagnosing,prognosing, treating, etc.) particular diseases, will now be discussed.

Breast Cancer—Patients and Methodology

Studies were first performed to identified signatures that could be usedto assess breast cancer. In total 1113 baseline samples were used, 935being from blood and 170 being from tissue samples. The samples wereanalyzed by the same, standardized, targeted quantitative massspectrometry technique at the same centralized and independentfee-for-service company (Biocrates, Austria).

The cancer groups (n=447) were composed by i) breast cancer volunteers(n=217) comprising pT1pN0 (n=68), pT1N1 (n=77), pT2N1 (n=8), T2N0M0(n=1) and T3N2M0 (n=63)]. Intrinsic subtypes were: i-luminals A (n=33),B (n=98), B-HER2 (n=23), triple negatives (n=37), HER-2 (n=14) andRE-/PR- (n=4). European patients (n=154) were composed by aretrospective (n=62) and a prospective arm (n=92) in addition to ii)lung (n=23), iii) head and neck (n=56), iv) liver (n=30), v)hematological malignancies (n=65) and vi) colon cancer patients (n=85)together to respective normal (n=85) and tumor tissues (n=85). Coloncancer patients were T1N0M0 (n=9), T2N0M0 (n=15), T3N0M0 (n=20), T3N0M1(n=1), T3N1M0 (n=10), T3N1M1 (n=6), T3N2M0 (n=6), T3N2M1 (n=7), T4N0M0(n=2), T4N1M0 (n=1), T4N1M1 (n=3), T4N2M0 (n=2), T4N2M1 (n=3).

The remaining 666 samples were included as control groups, out of which:169 controls (79 women and 90 men) were from the São PauloPopulation-based Health Investigation Project (ISA 2008), high risk ofbreast cancer development at baseline (n=48) and 1 year later (n=27),histologically proven non-invasive in situ carcinoma (n=21), low risk ofbreast cancer development (n=31) were defined as women with completenormality from mammographic (BIRADS 1) as well as ultrasoundperspectives and additionally scoring lower Gail Index values (<1.67),polycystic ovary syndrome (n=49), HIV-infected individuals prior oftreatment (n=18), hemolytic disorders-paroxysmal nocturnalhemoglobinuria (n=31) and autoimmune hemolytic anemia (n=27), cirrhosis(n=30), hyper (n=8) and hypo thyroid (n=8) function and engraftment dayof patients submitted stem cell allogeneic transplant (n=29).

Breast cancer patients with locally regional advanced tumors T3N2M0(n=63), were scheduled to receive a neoadjuvant chemotherapy approachcomprised of 4 cycles of doxorubicin (60 mg/m2) and cyclophosphamide(600 mg/m2), followed by 4 cycles of paclitaxel (175 mg/m2) conducted atthe Barretos Cancer Hospital.

This part of the study was designed to have as an endpoint theidentification of predictive signatures of tumor response in patientswith stage III disease, during the accomplishment of the project“Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer (LABC)”(clinical trials NCT00820690). Patients had a baseline assessment within2 weeks before starting chemotherapy, hematological andnon-hematological toxicities were recorded through complete bloodcounts, liver and kidney function as well as clinical evaluations ateach cycle (one every 3 weeks and one month after the end of treatment.

Baseline tumor dimensions were calculated using clinical andradiological measurements and compared to the final tumor diameter thatwas recorded directly on the surgery product by a dedicated pathologist.Complete Pathologic Response (pCR) was defined as no histopathologyevidence of any residual invasive and/or non-invasive disease in breastor nodes (ypT0/ypN0).

The same procedure was adopted to evaluate the metabolic signaturesidentified herein to different benign conditions in order to test howspecific and peculiar they were to breast cancer. To do so, comparisonswere assembled among breast cancer women to the following cancer-freegroups: A) Women at low risk of breast cancer development (n=31), B)Population-based controls (79 women and 90 men), C) Autoimmune disease(n=27), D) High risk of breast cancer development (n=36), E) Polycysticovary syndrome (n=49), F) HIV-infected individuals prior of anytreatment (n=18), G) Chemically-induced immune suppression of patientson the engrafting day after bone marrow heterologous stem celltransplantation (n=26).

In order to test whether metabolic deviations detected in stage IIIbreast cancer women could play any role in disease-free survival, theinitial findings were challenged against an earlier stage study the“Risk Prediction of Breast Cancer Metastasis Study” (Italy and Austria).The study was designed to have as an endpoint the identification ofmetabolic signatures of five years survival outcomes and included atotal of 154 cases classified as luminal (75.3%, 116/154) andnon-luminal (24.6%, 38/154) during a prospective (n=92) andretrospective (n=62) arms of women comprising pT1pN0 (n=68), pT1N1(n=77), pT2N1 (n=8), T2N0M0 (n=1).

Comparison of breast cancer metabolomics to population-based controlswas conducted to further explore the hypothesis that the initial resultscould be related to inborn errors of metabolism. Therefore, comparisonwas conducted against 169 age-matched men and women, with availableblood samples at baseline, of the São Paulo Population-based HealthInvestigation Project (ISA 2008) designed to prospectively analyze theuse of public health service in the city of São Paulo, SP, Brazil.

In order to identify any metabolic resemblances between breast cancersignatures and women at elevated risks of breast cancer development, ourfindings were challenged against a group of 41 women exhibiting relativerisks ranging from 1.2 to 2.0 in addition to a group of PCOS womendepicting HOMA-IR>2.5 (n=8) and <2.5 (n=10).

Participants completed a health history questionnaire, includinginformation on race, age at menarche, age at first live birth, number ofbiopsies, presence of atypical hyperplasia, and family history of breastand ovarian cancer. Using the Breast Cancer Risk Assessment Tool(BCRAT), the 5-year absolute and relative risks (RRs) of breast cancerwere estimated using source code version 3.0 from the National CancerInstitute website.

To explore the possibility of breast cancer being followed by energymetabolism deficiencies, the Lac/Pyr molar ratio was applied to the 154European breast cancer patients adopting cutoff values >25 to suggestpatients as harboring a primary (or secondary) respiratory chaindysfunction. To do so, besides quantification of 186 metabolitesquantified in all participants, there was additional quantification(mmol/L) of the following metabolites in blood of the 154 Europeanbreast cancer participants: lactate, pyruvate-oxaloacetate, succinate,fumarate and alphaketoglutarate by using the same mass spectrometryapproach adopted to the other measurements.

To gain accesses on how closer to metabolic stemness our results couldbe, our findings were compared to human embryo culture media used duringassisted reproduction procedures. Culture was performed followingroutine protocols (Borges et al 2015) adopted for intracytoplasmic sperminjection (ICSI) procedures in the Reproduction Section at the FederalUniversity of São Paulo, Brazil. After uterine transfer of embryos, theremaining embryo-free media were immediately frozen and kept at −80° C.until be analyzed by the same targeted quantitative MS/MS approach.Samples were divided into groups based on their degree of expansion andhatching status on day 3. Thus, two pooled groups comprising culturemedia of embryos of poor (n=100) and good development (n=100) wereassembled and submitted to mass spectrometry analysis.

To further confirm the theory as well as to validate ratios as proxiesfor enzyme activity related to insulin-dependent MYC activation, miceobtained from the Centro de Desenvolvimento de Modelos Experimentaispara Medicina e Biologia (CEDEME) of the Universidade Federal de SãoPaulo were maintained at a 12-hr light-dark cycle with ad libitum accessto tap water and chow diet. Dietary calories restriction was performedaccording to the protocol of the National Institute on Aging. Briefly,12-week old mice were divided in two groups: the ad libitum group, whichhad free access to the NIH31 diet (Harlan-Teklab) throughout the wholeprotocol, or the dietary restriction group, which was fed the NIH31/NIAFortified (Harlan-Teklab) starting with a 10% decrease in caloric intakefor a week, increased to 25% restriction in the following week, and to40% restriction until the end of the protocol. Food intake and bodyweight were assessed weekly. Protocols for animal use were approved bythe IACUC of the Universidade Federal de São Paulo (CEP-0218/11,CEP-0237/12 and CEUA4603261015) and were in accordance with NIHguidelines.

Absolute quantification (μmol/L) of blood metabolites was achieved bytargeted quantitative profiling of 186 annotated metabolites byelectrospray ionization (ESI) tandem mass spectrometry (MS/MS) in 302plasma samples, blinded to any phenotype information, on a centralized,independent, fee-for-service basis at the quantitative metabolomicsplatform from BIOCRATES Life Sciences AG, Innsbruck, Austria.

The experimental metabolomics measurement technique which included atargeted profiling scheme was used to quantitatively screen for fullyannotated metabolites using multiple reaction monitoring, neutral lossand precursor ion scans. Quantification of metabolite concentrations andquality control assessment was performed with the MetIQ software package(BIOCRATES Life Sciences AG, Innsbruck, Austria) in conformance with 21CFR Part 11, which implies proof of reproducibility within a given errorrange. An xls file was then generated, which contained sampleidentification and 186 metabolite names and concentrations with the unitof μmol/L of plasma.

For metabolomic data analysis, log-transformation was applied to allquantified metabolites to normalize the concentration distributions anduploaded into the web-based analytical pipelines MetaboAnalyst 3.0(www.metaboanalyst.ca) and Receiver Operating Characteristic CurveExplorer & Tester (ROCCET) available at http://www.roccet.ca/ROCCET (Xiaet al 2013) for the generation of uni and multivariate ReceiverOperating Characteristic (ROC) curves obtained through Support VectorMachine (SVM), Partial Least Squares-Discriminant Analysis (PLS-DA) andRandom Forests as well as Logistic Regression Models to calculate OddsRatios of specific metabolites.

ROC curves were generated by Monte-Carlo Cross Validation (MCCV) usingbalanced sub-sampling where two thirds (⅔) of the samples were used toevaluate the feature importance. Significant features were then used tobuild classification models, which were validated on the ⅓ of thesamples that were left out on the first analysis. The same procedure wasrepeated 10-100 times to calculate the performance and confidenceinterval of each model.

To further validate the statistical significance of each model, ROCcalculations included bootstrap 95% confidence intervals for the desiredmodel specificity as well as accuracy after 1000 permutations and falsediscovery rates (FDR) calculation.

In total, 186 annotated metabolites were quantified using the p180 kit(BIOCRATES Life Sciences AG, Innsbruck, Austria), being 40 acylcanitines(ACs), 21 amino acids (AAs), 19 biogenic amines (BA), sum of hexoses(Hex), 76 phosphatidylcholines (PCs), 14 lyso-phosphatidylcholines(LPCs) and 15 sphingomyelins (SMs). glycerophospholipids were furtherdifferentiated with respect to the presence of ester (a) and ether (e)bonds in the glycerol moiety, where two letters denote that two glycerolpositions are bound to a fatty acid residue (aa=diacyl, ae=acyl-alkyl),while a single letter indicates the presence of a single fatty acidresidue (a=acyl or e=alkyl). In the same company (Biocrates), theEuropean participants had their samples additionally analyzed for thefollowing energy metabolism metabolites: lactate, pyruvate/oxaloacetate,alpha ketoglutarate, fumarate and succinate.

In addition to individual quantification, groups of metabolites relatedto specific functions were analyzed. Groups of AAs were computed bysumming the levels of AA belonging to certain families or chemicalstructures depending on their functions such as the sum of: 1) essentialamino acids (Essential AA), 2) non-essential amino acids (non-EssentialAA), 3) glucogenic (Ala+Gly+Ser) amino acids (Gluc AA), 4)branched-chain (Leu+Ile+Val) amino acids (BCAA), 5) Aromatic(His+Tyr+Trp+Phe) amino acids (Arom AA), 6) Glutaminolytic derivatives(Ala+Asp+Glu), and 7) the sum of total amino acids.

Groups of acylcarnitines (AC), important to evaluate mitochondrialfunction, were also computed by summing Total AC, C2+C3, C16+C18,C16+C18:1 and C16-OH+C18:1-OH). Groups of lipids, important to evaluatelipid metabolism, were also analyzed by summing: 1) Totallysophosphatidylcholines (total LPC), 2) Total acyl-acyl and 3) Totalacyl-alkyl phosphatidylcholines (total PC aa and total PC ae,respectively), 4) Total sphingomielins (total SM) and 5) Sum of total(LPC+PC aa +PC ae +SM) lipids (Structural lipids).

Proportions among sums of saturated, monounsaturated and polyunsaturatedstructural lipids were also assembled as proxies to quantify elongasesand desaturases activities towards ether lipids: 1) Desaturase 9 [(PC aeC36:1+PC ae C38:1+PC ae C42:1)/(PC ae C42:0)], Desaturase 6 [(PC aeC44:6+PC ae C44:5+PC ae C42:5+PC ae C40:6+PC ae C40:5+PC ae C38:6+PC aeC38:5+PC ae C36:5)/(PC ae C36:1+PC ae C38:1+PC ae C42:1)].

Clinical indicators of liver metabolism and function were obtained bythe applying either the classical(leucine+isoleucine+valine/(tyrosine+phenylalanine) or variations(Val/Phe, Xleu/Phe) of the Fischer's quotient. Clinical indicators ofisovaleric acidemia, tyrosinemia, urea cycle deficiency and disorders ofβ-oxidation were calculated by adopting the ratios of valerylcarnitineto butyrylcarnitine (C5/C4), tyrosine to serine (Tyr/Ser), glycine toalanine and glutamine (Gly/Ala, Gly/Gln) and lactate to pyruvate(Lac/Pyr), respectively.

Proxies for enzyme function related to the diagnosis of very long-chainacyl-CoA dehydrogenase (VLCAD) and type two carnitine-palmitoyltransferase (CPT-2) deficiencies were achieved by assembling the ratios(C16+C18:1/C2), (C14:1/C4), (C14:1-OH/C9), (C14/C9), (C14:1/C9) and tothe elongation of very-long-chain-fatty acids (ELOVL2) (PC aa C40:3/PCaa C42:5). Levels of methionine sulphoxide (Met-SO) alone or incombination to unmodified methionine (Met-SO/Met) as well as symmetric(SDMA), asymmetric (ADMA) and total dimethylation of argine residues(Total DMA) were quantified to gain access to ROS-mediated proteinmodifications as well as to systemic arginine methylation status,respectively.

To gain access to MYC activity in blood, specific quantification ofmetabolites and ratios resulting from MYC-responsive enzymes activitieswere performed in the blood of hypoglycemic mice before and afterinsulin administration as well as in cancer-free women depicting normal(<2.5) and elevated (>2.5) HOMA (IR) values. Knowing that liverinhibition of gluconeogenesis is a bona fide insulin-MYC-dependentbiochemical reaction, a shift from normal to lower values in the ratioof glucose to glucogenic amino acids (Glucose/Ser, Glucose/Gly andGlucose/Ala) after insulin administration as well as in women withelevated HOMA (IR) values, was adopted as a measurement ofinsulin-MYC-related activity.

The same procedure was then applied to other MYC-responsive enzymes asfollows: arginine methyltransferases (ADMA, ADMA/Arg, SDMA, SDMA/Arg andTotal DMA, Total DMA/Arg), ornithine decarboxylase (Glu, Glu/Orn, Pro,Pro/Orn, Orn, Orn/Arg, Putrescine, Putrescine/Orn, Spermidine,Spermidine/Putrescine, Spermine and Spermine/Spermidine), alanine aminotransferase (Ala), (Ala/Glu), aspartate aminotransferase (Asp) and(Asp/Glu), glutaminase (Glu), (Gln/Glu), [(Glu+Asp+Ala)/Gln],[(Gln/Glu)/Asp], (Glu/Glucose)/(Ala/Glu) and[(Glu/Gln)/Glucose]/(Ala/Glu).

The later 2 ratios were specifically assembled based on in vitroexperiments related to the “glutamate pulling effect” which is definedas the hypoglycemia-induced up-regulation in the deaminated, rather thantransaminated, production of glutamate through insulin-MYC-dependentglutamate dehydrogenase (GDH) stimulation of glutaminolysis withconsequent increased amounts of net keto acids to anaplerosis. Becauselower microenvironmental pH values ate also reported to favor the“glutamate pulling effect” we also calculated the degree of correlationbetween increases in lactate and glutamate by Pearson r correlationanalysis.

The ratios of serine to C2 (Ser/C2) and of (PC aa C42:0/PC ae C32:3)were additionally included as proxies for glycolysis-relatedphosphoglycerate dehydrogenase (PHGDH) and glucokinase regulator (GCKR)activities. The later inhibits glucokinase activity in liver andpancreas and the former catalyses the rate limiting step of serinebiosynthesis. In parallel, and assuming the ratio values of glutamine toglutamate (Gln/Glu) and to aspartate [(Gln/Glu)/Asp], as proxys forglutaminolytic activity, the inventors assembled the ratios[(Ser/C2)/(Gln/Glu)], [(Ser/C2)/(Gln/Glu)/Asp], [(PC aa C32:2/PC aeC34:2)/(Gln/Glu)] and [(PC aa C32:2/PC ae C34:2)/(Gln/Glu)/Asp] astheoretical equations to gain access to the balance between glycolysisand glutaminolysis.

Other Diseases—Patients and Methodology

In light of the foregoing, studies were then performed to identifiedsignatures (i.e., other signatures) that could be used to assess otherdiseases, such as ovarian cancer, colorectal cancer, pancreatic cancer,and acute graft-versus-host disease, to name a few.

In certain embodiments, the disease at issue is cancer (e.g., ovariancancer, etc.), and may be at a particular stage (e.g., stages I, II, IIIor IV). Definition of the medical stages of cancer is defined by theAmerican Joint Committee on Cancer (AJCC) of the United States NationalCancer Institute at the National Institutes of Health. The stagingsystem provides a strategy for grouping patients with respect toprognosis. Therapeutic decisions are formulated in part according tostaging categories but primarily according to tumor size, lymph node anddistant metastasis status.

Regardless of the disease at issue, the biological sample is obtainedfrom a mammal, preferably from a mouse, a rat, a guinea pig, a dog, amini-pig, or a human, most preferably human, further preferably from awoman. The biological sample preferably is blood, however, any otherbiological sample known to the skilled person, which allows themeasurements according to the present invention is also suitable. Theblood sample typically is full blood, serum or plasma, wherein bloodplasma is preferred. Dried samples collected in paper filter are alsoaccepted. Thus, the methods according to the invention typically are invitro methods.

For the measurement of the metabolite concentrations in the biologicalsample a quantitative analytical method such as chromatography,spectroscopy, or mass spectrometry is employed, where chromatography maycomprise GC, LC, HPLC, and UPLC, spectroscopy may comprise UV/Vis, IR,and NMR, and mass analyzers/spectrometry may comprise ESI-QqQ,ESI-QqTOF, MAL¬DI-QqTOF, and MAL¬DI-TOF-TOF. More preferably, massanalyzers/spectrometry comprises Quadrupole Mass Analyzers, Ion TrapMass Analyzers, TOF (Time of Flight) Mass Analyzer, Orbitrap massanalyser, Magnetic Sector Mass Analyzer, Electrostatic Sector MassAnalyzer, Ion Cyclotron Resonance (ICR) and combinations of massanalyzers, including single quadrupole (Q) and triple quadrupole (QqQ),QqTOF, TOF-TOF, Q-Orbitrap. The inventors have discovered that use ofFIA- and HPLC-tandem mass spectrometry is preferred and has certainbenefits.

Abbreviations that are used herein are as follows: GC=GasChromatography, CE=Capillary electrophoresis, LC=Liquid Chromatography,HPLC=High Preasure Liquid Chromatography, UHPLC=Ultra High PreasureLiquid Chromatography, UV-Vis=Ultraviolet-Visible, IR=Infrared, NIR=NearInfrared, NMR=Nuclear Magnetic Ressonance, ESI=Electron SprayIonization, MALDI=Matrix-assisted laser desorption/ionization,TOF=Time-of-Flight, APCI=Atmospheric pressure chemical ionization,QqQ=Triple quadrupole configuration also known as Q1q2Q3 (Q1 and Q3quadrupoles are mass filters and q2 is a no mass-resolving quadrupole).

For measuring the metabolite amounts targeted metabolomics is used toquantify the metabolites in the biological sample including the analyteclasses of amino acids, biogenic amines, acylcarnitines, hexoses,sphingolipids and glycerophospholipids. The quantification is done usingin the presence of isotopically labeled internal standards anddetermined by the methods as described above. A list of analytesincluding their abbreviations (BC codes) being suitable as metabolitesto be named according to the invention is indicated in FIGS. 2-6.

In order to reach the highest capability to detect a disease usingmetabolomics, the present invention identified its discriminantbiochemical features and ratios not only by comparing sick patients(i.e., ones having a particular disease, such as ovarian cancer) tohealthy controls but also to a larger group of participants with othermalignant and benign conditions. Samples were prospectively collectedand analyzed by the same, fee-for-service, standardized, targetedquantitative mass spectrometry technique at the same centralized andindependent company (Biocrates, Austria).

A group of plasma samples of woman having certain cancers at variousstages (i.e., stage I, II and III) with no previous treatment wereincluded, the cancer patients (n=473) were composed by: i) breast cancervolunteers from Brazil and Europe (n=213) in addition to ii) lung(n=23), iii) head and neck (n=56), iv) liver (n=30), v) hematologicalmalignancies (n=65), and vi) colon cancer patients (n=85) together torespective normal (n=85) and tumor tissues (n=85).

The remaining 752 samples were included as control groups, out of which:169 controls (79 women and 90 men) were from the São PauloPopulation-based Health Investigation Project (ISA 2008) that due to itspopulation characteristics, allowed us to analyzed them according theirfrequency of metabolic syndrome distributed according the 6 progressivestages following the recommendation of the Joint Interim Statement ofthe International Diabetes Federation Task Force on Epidemiology andPrevention; National Heart, Lung, and Blood Institute; American HeartAssociation; World Heart Federation; International AtherosclerosisSociety; and International Association for the Study of Obesity.

Controls also included 33 women at elevated risks of breast cancerdevelopment, 23 participants with histologically proven non-invasive insitu carcinoma, 31 women at low risk of breast cancer development, 49with polycystic ovary syndrome, 18 HIV-infected individuals prior oftreatment, 34 women with rheumatoid arthritis, 58 autoimmune hemolyticdisorders, 30 participants with cirrhosis, 8 with hyper and 8 withhypothyroidism.

Targeted (ESI-MS/MS) Quantitative Metabolomics/Lipidomics profiling, wasperformed in an independent validation set with plasma samples fromwoman with various cancers as well as a number of controls, on twoindependent, fee-for-service basis using quantitative metabolomicsplatform at Biocrates Life Sciences AG, Innsbruck, Austria and QuestDiagnostics Nichols Institute San Juan Capistrano, Calif., USA.

Briefly, a targeted profiling scheme was used to quantitatively screenfor known small molecule metabolites using multiple reaction monitoring,neutral loss and precursor ion scans. Quantification of the metabolitesof the biological sample is achieved by reference to appropriateinternal standards and the method has been proven to be in conformancewith 21 C.F.R., Part 11, which implies proof of reproducibility within agiven error range. Concentrations of all analyzed metabolites werereported in μM.

In total, 186 different metabolites were been detected being 40acylcanitines, 19 proteinogenic aminoacids, ornithine and citrulline, 19biogenic amines, sum of Hexoses, 76 phosphatidylcholines, 14lyso-phosphatidylcholines and 15 sphingomyelins. See FIGS. 2-6.

Glycerophospholipids are further differentiated with respect to thepresence of ester (a) and ether (e) bonds in the glycerol moiety, wheretwo letters (aa=diacyl, ae=acyl-alkyl, ee=dialkyl) denote that twoglycerol positions are bound to a fatty acid residue, while a singleletter (a=acyl or e=alkyl) indicates the presence of a single fatty acidresidue.

Lipid side chain composition is abbreviated as Cx:y, where x denotes thenumber of carbons in the side chain and y the number of double bonds,e.g. “PC ae C38:1” denotes a plasmalogen/plasmenogen phosphatidylcholinewith 38 carbons in the two fatty acid side chains and a single doublebond in one of them.

Training cases were used for marker discovery and to identify anyclinical variable that might be associated with a particular disease bylogistic regression analysis. Quantification of metaboliteconcentrations and quality control assessment was performed with theMetIDQ® software package (BIOCRATES Life Sciences AG, Innsbruck,Austria). Internal standards serve as the reference for the metaboliteconcentration calculations. An xls file was then exported, whichcontained sample names, metabolite names and metabolite concentrationwith the unit of μmol/L of in plasma.

Data was then uploaded into the web-based analytical pipelineMetaboAnalyst 2.0 (www.metaboanalyst.ca) and normalized usingMetaboAnalyst's normalization protocols (Xia et al 2012) for uni andmultivariate analysis (see above discussion concerning normalization),high dimensional feature selection, clustering and supervisedclassification, functional enrichment as well as metabolic pathwayanalysis.

Data was also imported to ROCCET (ROC Curve Explorer & Tester) availableat http://www.roccet.ca/ROCCET/ for the generation of uni andmultivariate Receiver Operating Characteristic (ROC) curves obtainedthrough Support Vector Machine (SVM), Partial Least Squares-DiscriminantAnalysis (PLS-DA) and Random Forests.

Curves were generated by Monte-Carlo cross validation (MCCV) usingbalanced subsampling where two thirds (⅔) of the samples were used toevaluate the feature importance. Significant features were then used tobuild classification models, which were validated on the ⅓ of thesamples that were left out. The same procedure was repeated multipletimes to calculate the performance and confidence interval of eachmodel.

Breast Cancer Signature

Breast cancer remains a leading cause of morbidity and mortalitythroughout the world. Earlier diagnosis through the application ofmammography and magnetic resonance imaging has improved the detection ofsmaller volume disease providing physicians the opportunity to interveneat earlier stages when the cancers are most curable.

The advent of molecular technologies, widely applied in prognosticdeterminations, have evolved into diagnostic tools that utilizecirculating tumors cells and cell free DNA for earlier detection,prognosis and where applicable response prediction. Numerous clinicaltrials are now exploring the clinical utility of these approaches.

Research shows that human cancers evolve in an environment of metabolicstress. Rapidly proliferating tumor cells deprived of adequate oxygen,nutrients, hormones and growth factors up-regulate pathways that addressthese deficiencies to overcome hypoxia (HIF), vascular insufficiency(VEGF), growth factor deprivation (EGFR, HER2) and the loss of hormonalsupport (ER, PR, AR) all to enhance survival and proliferation.

Many oncogenes are now known to regulate metabolic pathways that arecritical for cell survival in the inhospitable tumor micro-environment,where oxygen and nutrient sources are highly limited. Indeed RAS, PI3K,TP53 and MYC among others are now recognized to be important metabolicregulators whose functions are fundamental for tumor cell survival.

Based upon the growing recognition that cancer cells differ from theirnormal counterparts in their use of nutrients, synthesis of biomoleculesand generation of energy, we applied quantitative mass spectrometry tothe blood and tissue of patients with breast cancer and compared theresults with those observed in normal controls. To explore commonalties,the inventors extended these studies to include other cancers ofglandular and non-glandular ancestries and to non-malignant diseasestates associated with metabolic stress including poly cystic ovarysyndrome and advanced metabolic syndrome.

The findings led to a murine model of insulin/glucose mediation ofmetabolic stress and finally to an exploration of the secretome of humanembryos prior to implantation to examine the “stemness” of the signalsobserved.

To evaluate the possible differences and likenesses among breast cancerand tumors of distinctive histology and locations, the identified bloodsignatures were compared to blood samples from treatment-naive lung(n=23), plasma from prostate cancer patients (n=10), head and neck(n=57), liver (n=30) and colon cancer patients (n=85), the latter withrespective normal (n=85) and tumor tissues (n=85) as well ashematological malignancies (n=36). Normal (n=85) and tumor tissues(n=85) from patients harboring colon cancer, were also used to validate,at tissue level, the metabolic communalities identified in blood andshared by breast and colon cancer.

Important, each individual type of cancer, besides comparison todifferent malignancies, were also compared to the group of 666 controlsdescribed above containing 12 different benign metabolic conditions.After multivariate ROC curve analysis, the ratios shown in FIGS. 7A andB emerged as the representation of the metabolic scenario depicting thehighest specificity to each individual tumor.

The search for metabolic intermediates, the blood concentrations ofwhich (μM/L) could be utilized as breast cancer biomarkers led to theassembly of an exploratory data set that compared plasma samples fromwomen at low risk of breast cancer (n=31) with plasma samples frompatients with treatment-naive stage III (T3N2M0) invasive disease(n=59). Targeted quantitative MS/MS analysis (1) coupled withunsupervised clustering analysis (online methods) identified clearmetabolic differences between cases and controls (FIG. 1a ). Validationwas then undertaken (statistical power=0.8) that compared 169population-based control samples, against results obtained in 154 casesfrom an independent and earlier reported disease cohort the “RiskPrediction of Breast Cancer Metastasis Study” (Italy and Austria).

Results demonstrated that breast cancer women exhibited at least one up-or down-regulated metabolite from amongst the 5 principal classes ofmetabolites that were quantified in blood. Statistical analysis depictsthe individual validation of nine of these metabolites, originallyidentified in the exploratory phase including glutamine (Gln), aspartate(Asp), glutamate (Glu), lysophosphatidylcholine acyl C26:1 (lysoPC aC26:1), Sphingomyelin C18:0 (SM C18:0), 3-Hydroxytetradecenoylcarnitine(C14:1-OH), phosphatidylcholine acyl-alkyl C38:3 (PC ae C38:3),methionine sulfoxide (Met-SO) and taurine.

Among the observations in both, the exploratory and the validation sets,was the finding that glutamine concentrations in the cancer patientswere reduced to nearly ⅛ of the levels observed in the normal population(˜800 μM/L) (p=7.8e-53, FDR=2.7e-52), while blood concentrations ofaspartate (p=1.7e-67, FDR=8.3e-67) (FIG. 1e ) and glutamate (p=6.4e-96,FDR=6.2e-95) were nearly 10 fold higher than the normal ranges of 0-5μM/L and 40 μM/L, respectively.

As glutamine consumption associated with parallel increases in glutamateand aspartate is considered a hallmark of MYC-driven “glutaminolysis,”these findings led an examination of other MYC-associated phenomena tointerrogate the observations.

Hepatic glutamine (Gin) metabolism regulates the level of amino acids inthe circulation and Glutamate (GLU) through its role in numeroustrans-deamination reactions is central to this process.

As MYC activation is associated with measurable changes in blood levelsof specific metabolites including glutamine, glutamate, the ratiosthereof and others, targeted quantitative MS/MS was used to evaluate(μM/L) these intermediates as surrogate markers for MYC activation. Theinventors then assembled metabolite ratios measured directly in blood toserve as “proxies” for MYC-coordinated metabolic functions.

In agreement with their hypothesis, the Gln/Glu ratio, a negativesurrogate for glutamine metabolism, i) discriminated breast cancer casesfrom controls; ii) inversely correlated (Correlation=−0.54, p=3.67e-6FDR=3.06e-5) with elevated breast cancer risk; iii) correlated with therisk of 5-year mortality in pathological stage I patients, and iv)inversely correlated with the failure to achieve pathologic completeremission (pCR) after neo-adjuvant chemotherapy (NAC)(Correlation=−0.81, p=1.15e-81, FDR=2.13e-80).

Parallel analyses found that the Gln/Glu ratio inversely correlates withi) late stage metabolic syndrome and with ii) increased chance of deathin both the retrospective and prospective arms of the European cohort(Correlation=−0.68, p=2.30e-38, FDR=1.59e-37).

Theoretically, changes in glutamine consumption, reflected by theGln/Glu ratio could provide a metabolic link between breast cancerinitiation and diabetes, reflective of a systemic metabolicreprogramming from glucose to glutamine as the preferred source ofprecursors for biosynthetic reactions and cellular energy.

The inventors found the same changes in the Gln/Glu ratio in nearly 100%of breast cancer patients, independent of intrinsic subtype. Thesebreast cancer patients revealed systemic MYC-associated biochemicalshifts, previously described in vitro, associated with glutamineutilization over glucose for the synthesis of structural phospholipids,as measured by the ratios (Structural Lipids/Gln) and (StructuralLipids/Hexoses), respectively. The MYC signatures in breast cancerpatients and their similarity to diabetes mellitus raised the questionwhether metabolic re-programming might be identified through themeasurement of other bio-chemical intermediates.

To examine breast cancer against other disease states, the results werecompared with those obtained from other cancers (30 liver; 23 lung; 85colon; 58 head & neck and 65 hematologic) and from individuals withvarious metabolic conditions including late stages of metabolic syndrome(n=70), HCV-induced cirrhosis (n=30); hyperthyroidism (n=8);hypothyroidism (n=8); HIV infection (n=18); polycystic ovary syndrome(n=49); auto immune disease (n=86) and with those from women at elevatedrisk for breast cancer (n=33).

The inventors measured biochemically-active metabolites that hadpreviously been described in large metabolomics and genome-wideassociation studies to examine established single metabolite andmetabolite ratios related to: i) liver function (Val/Phe, Xle/Phe), ii)lipid desaturase activity (PC aa C36:6) and iii) serinepalmitoyltransferase (SPTLC3) activity (PC aa C28:1 and C10:2). Thesemeasures were used to develop algorithms for the interrogation of thedata sets.

Results, as multivariate Receiver Operator Curve (ROC) analyses, usingthe equation {[PC aa 36:6/[(Val/Phe)/Taurine]/C10:2} and the lipid PC aaC28:1, were found to segregate breast cancer from controls, irrespectiveof stage (I to III) and intrinsic subtypes, in both the exploratory[AUC=0.987 (95% CI: 0.964-1), sensitivity=96.72%, specificity=96.78%,positive predictive value=98.33%, negative predictive value=93.94%,average accuracy (100-fold cross validations)=0.95 and predictiveaccuracy statistics (1000 permutations)=p<2.04e-05] and validation sets[AUC=0.995 (95% CI: 0.991-0.998), sensitivity=98.09%,specificity=96.18%, positive predictive value=82.35%, negativepredictive value=99.64%, average accuracy (100-fold crossvalidations)=0.96 and predictive accuracy statistics (1000permutations)=p<1.28e-06].

To confirm these associations, Pearson's r correlations(www.metaboanalyst.ca) were conducted that compared the described ratiovalues with levels of the oncometabolites fumarate, succinate, lactate,glutamine and hexoses measured in the blood of our 154 European breastcancer patients. The highest positive correlations were found withlactate (p=1.42e-08, FDR=3.24e-07), lactate/pyruvate (p=7.96e-06,FDR=7.47e-05), fumarate/hexoses (p=0.0004, FDR=0.002), succinate/hexoses(p=0.0001, FDR=0.0007) and the glutaminolysis-related ratio(Ala+Asp+Glu/Gln) (p=0.0004, FDR=0.002). When the (Lac/Pyr) values wereapplied to the logistic regression equation logit(P)=log[P/(1−P)]=−12.24+1.80 Lac/Pyr, where P is Pr(y=1|x), elevationsin this ratio were associated with an increased risk of 5-years death(Odds=6.08 [Pr (>|z|)=0.001]) when analyzing patients with primarytumors not bigger than 2.0 cm (n=103).

The highest negative correlations were observed for hexoses/lactate(p=5.88e-08, FDR=1.11e-06); hexoses (p=0.002, FDR=0.007); and the livergluconeogenesis ratios (hexoses/PHGDH Act) (p=0.002, FDR=0.007); and(hexoses/Ala+Gly+Ser) (p=0.0014, FDR=0.005); [hexoses/(C14:1/C4)](p=0.003, FDR=0.009); [hexoses/(C18:1/C8)] (p=9.94e-05, FDR=0.0006);(hexoses/CPTII) (p=0.0007, FDR=0.003); [hexoses/(C16/C3)] (p=0.001,FDR=0.004); (hexoses/AcylC-DC) (p=0.002, FDR=0.007).

When the metabolic profiles of patients with different tumors (lung,colon, liver, leukemias, lymphomas and squamous cells carcinoma of headand neck) were examined, the results again demonstrated enhancedglutamine consumption, particularly in patients harboring tumors ofglandular ancestries.

Extending these studies to include patients with polycystic ovarysyndrome (PCOS), cirrhosis, high-risk of breast cancer and stage 5metabolic syndrome revealed that these cancer-free participantsmanifested glutaminolytic profiles that were very similar to those foundin adenocarcinoma patients.

The ratio (Glu/Hexoses) was assembled by us following the in vitrodemonstration of the “glutamate pulling effect” where glucose starvationin malignant cells culture leads to elevations in glutamate through aMYC-coordinated reaction.

This effect was clearly identified in the blood of patients harboringadenocarcinomas, those at higher risk of breast cancer and individualswith PCOS. Noteworthy, neither of the control groups composed ofpopulation-based normal controls or patients with non-glandular tumors(leukemias, lymphomas, multiple myelomas and squamous cell carcinomas)revealed marked changes in this ratio, particularly squamous cellcarcinomas that revealed similar levels to controls.

Increases in the “glutamate pulling effect” have been described underconditions of metabolic stress induced by glucose deprivation. Inagreement, the inventors found a significant (p=0.003, FDR=0.009)inverse correlation between patient blood hexoses concentrations and thevalues of their breast cancer equation {[PC aa36:64(Val/Phe)/Taurine]/C10:2}.

In line with the premise that glandular cancers are promoted underconditions of relative hypoglycemia, measured as the “glutamate pullingeffect,” their results suggest that the isolated determination of bloodglucose levels may not be as informative as the measurement of hexoselevels in relation to other metabolic intermediates including: i) themitochondrial carnitine palmitoyltransferase II (CPT-2) deficiency ratio(C16/C3), ii) the peroxisomal impairment biomarkers lysoPC a C26:0,lysoPC a C26:1 and lysoPC a C28:1, or iii) its relation toglutaminolysis [Phe/(Gln/Glu)/Asp]. Importantly, both CPT-2 andperoxisomal deficiencies, well known inborn errors of metabolism, areassociated with hypoglycemia in afflicted patients.

If a state of relative hypoglycemia were to occur in breast cancer asthe result of inborn-like errors of metabolism, then hyperinsulinemiaassociated with chronic hypoglycemia would constitute a powerfulmetabolic stressor capable of systemically up-regulating glycolysis andglutaminolysis, even in the absence of cancer.

To examine the hypoglycemia premise, the inventors developed anexperimental murine model in which insulin was administered to miceunder normo- and hypoglycemic conditions. In this murine model only thehypoglycemic mice that received insulin recapitulated the MYC-dependentshifts that had been observed in cancer patients, characterized by theinsulin/MYC-dependent reactions of i) glutaminolysis (Gln/Glu),(Ala/Glu) and [(Gln/Glu)/Asp] as well as glycolysis (Ser/C2) and thecombination of both (Ser/C2)/[(Gln/Glu)/Asp], ii) glutamate pullingeffect (Glu/Hexoses), iii) arginine methyltransferase activity [TotalDMA/[(Gln/Glu)/Asp] and [Tau/[(Gln/Glu)/Asp], iv) liver function[BCAA/(Phe+Tyr)], ornithine decarboxylase activity (Spermidine), v)liver gluconeogenesis [Hexoses/(Ala+Gly+Ser)] and vi) peroxisomalimpairment (lysoPC a C26:0).

To confirm these findings in humans, we examined whether bloodconcentrations of hexoses correlated with peroxisome dysfunction, asrepresented by the elevation of specific lipids containing very longchain fatty acids (VLCFA). “Pearson r” correlations were conducted tocompare women at low risk of cancer (n=31), to women at elevatedrelative risk (scoring 1.7 to 1.9) (n=14), women with non-invasive (insitu) carcinoma (n=23), women with polycystic ovary syndrome (n=49), andthose with invasive breast cancer both luminal (n=118) and non-luminal(n=36).

Results, from the ratios of hexoses to lysoPC a C26:1 (Correl.=−0.73,p=3.41e-49, FDR=2.89e-48) and hexoses to lysoPC a C28:1 (Correl.=−0.60,p=9.88e-30 and FDR=6.29e-29) demonstrated a progressive negativecorrelation beginning with women at high risk and in situ carcinoma, toPCOS and finally achieving a nadir in the plasma of patients withinvasive disease, irrespective of intrinsic subtype.

The results suggest that breast cancer could be preceded by systemicsubclinical disturbances in glucose-insulin homeostasis characterized bymild, likely asymptomatic, IEM-like biochemical changes. The processwould include variable periods of hyperinsulinemia with the consequentsystemic MYC activation of glycolysis, glutaminolysis, structurallipidogenesis and further exacerbation of hypoglycemia, the result ofMYC's known role as an inhibitor of liver gluconeogenesis.

Under normal conditions hypoglycemia results in the recruitment of fattyacids from storage pools. However, individuals who carry a primaryinability to utilize fatty acids as an energy source, as seen in FattyAcids Oxidation Defects (FAOD), would be prone to the accumulation oftoxic oncometabolites as well as carnitine and fatty acid derivativeswith increased ROS production and further mitochondrial disarrangement.

In this context, the metabolic dependencies of cancer characterized byexcessive glycolysis, glutaminolysis and malignant lipidogenesis,previously considered a consequence of local tumor DNA aberration could,instead, represent a systemic biochemical aberration that predates andvery likely promotes tumorigenesis.

Furthermore, these metabolic disturbances would be expected to remainextant after therapeutic interventions which is consistent with therecent observation that breast cancer relapse rates remain unaltered upto 24 years following initial treatments.

In support for our hypothesis and consistent with the definition of IEM,the inventors detected the accumulation of very long chainacylcarnitines such as C14:1-OH (p=0.0, FDR=0.0), C16 (p=0.0, FDR=0.0),C18 (p=0.0, FDR=0.0) and C18:1 (p=1.73e-322, FDR=1.16-321) and lipidscontaining VLCFA (lysoPC a C28:0) (p=1.14-e95, FDR=1.65e-95) in theblood of breast and colon cancer patients. Strikingly these sameprofiles were identified not only in the colon tumor tissues but also inthe adjacent normal colonic mucosa removed at the time of surgery fromthese same colon cancer patients.

The metabolic changes they describe in breast cancer arise in concertwith IEM-like changes in oxidative phosphorylation as detected byincreased values of the ratio lactate/pyruvate characteristic of Ox/Phosdeficiency. In the study, 76% (70/92) of the European breast cancerpatients had lactate/pyruvate ratios values higher than the normal valueof 25.8.

Recent reports have identified a four-fold higher frequency of cancer(including breast) in patients with energy metabolism disorders and IEMsare associated with elevated hexose/insulin disorders and gonadal andthyroid dysfunction that are themselves associated with highlactate/pyruvate ratios.

Defects in oxidative phosphorylation can occur as a result of primaryfatty acid oxidation deficiencies (FAOD) as they are associated with thesystemic mitochondrial accumulation of toxic fatty acid and carnitinederivative intermediates.

To determine whether excessive glutaminolysis and glycolysis, asquantified in the current study, reflect systemic rather than localevents, to was hypothesized that the identified oncogenic disturbancesshould be present in the normal tissues, other than blood, of patientswho harbor malignancies.

If true, then the biochemical profiles identified in these normal tissuebiopsies should provide similar prognostic information with regard toresponse and survival to the data generated directly from tumor biopsymaterial.

Among the most powerful metabolic equations for MYC-activation is thatwhich links the widely used MYC-driven desaturation marker ratio ofSFA/MUFA to the MYC glutaminolysis-associated ratio of (Asp/Gin). Theinventors prior experience in 213 breast cancer and 200 controlsrevealed that the metabolic deviation underscored by this equation[(SFA/MUFA)/(Asp/Gln)] is one of the most robust breast cancerdiscriminants (AUC=1.0, p=1.32e-127).

ANOVA and unsupervised clustering comparisons were assembled to comparethe blood metabolic phenotypes from controls (n=200), breast cancer(n=213) and colon cancer patients (n=85) with signatures obtained fromboth normal colonic epithelium (n=85) and colon cancers removedsurgically from the same 85 CRC patients.

These results demonstrate virtually identical biochemical phenotypes,revealed by this equation in the blood of breast and colon cancerpatients that are quantitatively indistinguishable from the phenotypicdeviations detected in the normal and colon tumor tissues. When comparedwith the control group (n=200), the results from blood or tissue (bothnormal mucosa and tumoral) of the cancer patients are so concordant asto represent virtually indistinguishable biological samples.

Interestingly, the biochemical disturbances found in the normal colonicmucosa reflected in the ratio {(Ser/C2)/[(Gln/Glu)/Asp]}, significantly(p=1.63e-33, FDR=2.21e-33) correlated with the risk of relapse at 5years indistinguishable from the results obtained with the colon tumorsfrom these patients. This ratio not only clearly distinguished breastcancers from controls as well as women at low and high risk of cancerbut also distinguished i) women with shorter (2.1 years) vs. longer (5.1year) relapse-free survival, and ii) women who achieved completepathological response (pCR) vs. patients with residual disease after NAC(p=3.73e-108, FDR=2.31e-107) (i.e., prognosis).

Additional observations in the present study found that liverdysfunction shares many features with both IEM and cancer suggesting arole for hepatic dysfunction in carcinogenesis.

Lower values of Fischer's quotient [(Ile+Leu+Val)/(Tyr+Phe) and ALTactivity (Ala/Glu), were found in cancer-free women with PCOS, thosewith elevated risks of cancer development and those with establishedglandular malignancies (liver, breast, colon, lung). These recurringbiochemical deviations include transamination and gluconeogenesisfrailties and the incapacity to properly metabolize branched chain(BCAA) and aromatic amino acids.

The metabolic shifts evidenced by lower values in Fischer's ratio werenot detected in any metabolic syndrome participant reflecting anaccumulation of BCAA in blood, mainly in later stage disease, whereinthe Fischer's ratios were found to be higher. In adenocarcinoma patientsthe lower values of Fischer's ratio seem to reflect a deterioration ofliver function resulting in a simultaneous diminution in BCAA and theaccumulation of aromatic amino acids. Indeed, phenylalanine levels inbreast cancer patients were found to be greater on average 89.3 μM/L (75to 128 μM/L) than the normal expected values (40 to 74 μM/L) in 55%(85/154) of European breast cancer patients. Women scoring relativerisks of 1.8 for breast cancer development also revealed elevated levelsat 82.8 μM/L (64.6 to 98.8 μM/L) especially when compared to low riskwomen 70.3 μM/L (46.5 to 97.9 μM/L) and late stage metabolic syndromewith an average of 68 μM/L (47 to 95 μM/L). Patients with thyroiddysfunctions also exhibited higher levels of phenylalanine 94.6 μM/L(49.5 to 142 μM/L). As expected, cancer-free participants with cirrhosisexhibited the highest levels averaging 114.3 μM/L (84.4 to 163 μM/L).

To confirm these findings as liver-function related the inventorsincluded cancer-free patients with HCV-induced cirrhosis (n=30) andpatients with hypo (n=8) and hyperthyroidism (n=8), as thyroiddysfunction is frequently associated with liver dysfunction and withincreased risk of cancer including breast. They also analyzed HIVpatients due to their increased risk of cancer and the direct effect ofHIV infection on liver function.

Results revealed concordance between the blood metabolic profiles ofcancer-free patients with cirrhosis, thyroid dysfunction and HIVinfection and the study participants at: 1) elevated relative risks ofbreast cancer development, 2) those with PCOS and 3) patients harboringknown glandular malignancies (breast, colon, lung and liver).

The inventors divided their cancer-free group according to: i)increasing risks of cancer, ii) rising levels of gamma-glutamyltransferase (GGT) and iii) cumulative values of free-thyroxine (FreeT4). The results revealed the same pattern of Gln/Glu ratios whenapplied to high risk women, was recapitulated in cancer-free women byprogressive changes in free-T4 and GGT values. Similar to thyroiddysfunctions, elevations in blood GGT have been found to significantlyincrease the overall cancer risk including breast malignancies. Toexplore the biochemical overlap between these conditions, the inventorsconducted Orthogonal Partial Least Squares Discriminative Analysis(Ortho-PLSDA) that revealed a high degree of biochemical similarityamong hyper/hypothyroidism and cirrhosis patients that, together, seemto interconnect breast cancer on the one side to hematologicalmalignancies on the opposite side.

It has previously been found that IEMs not only interfere with liverfunction but also affect proper endocrine physiology resulting inincreased risks of diabetes, gonadal and thyroid dysfunctions.

Results of the studies identifying liver dysfunction are in agreementwith the premise that breast cancer arises in an environment of fattyacid oxidation defects (FAOD). Among the most common laboratory findingsin these types of IEM, in parallel with hypoglycemia, is liverdysfunction as the biochemistry of the liver is so dependent on thenormal function of hepatocyte mitochondria.

The findings, therefore, resemble those associated with mitochondrialand/or peroxisomal disorders of β-oxidation, both known to be associatedwith the accumulation, in blood and tissues, of lipids composed of verylong-chain fatty acids (VLCFA) and carnitine derivatives, the result ofthe inefficient oxidation of fatty acids.

In line with this concept, when controls (n=92) were compared withbreast cancer patients (n=63) the untargeted mass spectrometry lipidomicdata showed a global accumulation of phospholipid species containingvery-long chain fatty acids (VLCFA C40) in the cancer patient specimens.

Of note are the blood elevations of lysoPC a C26:0, a biomarkerroutinely used in the diagnosis of peroxisomal disorders of β-oxidation.Validation of this finding was subsequently obtained by specifictargeted MS/MS (p=9.07e-71, FDR=2.81e-70). Further suggestion ofperoxisome as a putative subcellular location related to these metabolicfindings was obtained by quantitative functional enrichment analysis(www.metaboanalyst.ca) that revealed a significant (p=1e-121) 250-foldenrichment for peroxisome localization using the metabolitesL-acetylcarnitine, succinic acid, glycine, oxaloacetic acid, pyruvicacid, sarcosine, D-arginine and taurine.

An additional finding was the significant elevations of taurine in theblood of breast cancer patients and its association with cancer risk,response and survival as well as its correlation with blood levels ofthe oncometabolites fumarate (p=3.05e-06) and succinate (p=1.87e-05).

Both fumarate and succinate are known to increase the half-life of HIF-1gene (hypoxia-inducible factor-1) products that sponsor angiogenesis andtumor survival (33-36).

These oncometabolites also enhance histone and DNA methylation (37, 38)leading to genome-wide epigenetic reprogramming (39). Taurine levelswere also found to correlate (p=0.001, FDR=0.006) with the up-regulationof arginine methyltransferase activity, measured as the total amount ofdymethylated arginine residues (Total DMA).

Total DMA levels were also gradually, positively and statistically(p=5.57e-12, FDR=1.56e-11) associated with progressive stages of breastcarcinogenesis.

Arginine methyltransferase activity is directly connected to MYCactivity and has been reported to be associated to the state of cellularstemness.

This led to question whether the breast cancer findings were reflectiveof a state of cellular biochemical stemness, as it has been suggestedthat there are considerable parallels between human embryogenesis andcancer.

To evaluate this hypothesis, the inventors compared the breast cancermetabolomic signatures to those identified in the secretome of in-vitrofertilized, developing human embryos that were under final preparationfor implantation.

Results demonstrated strong similarities between the metabolic profilesof successfully developed embryos and the biochemical phenotypesidentified in women at high risk of breast cancer, those with insulinresistance and those with the shortest relapse-free survival followingneoadjuvant chemotherapy.

The invention includes a new concept of carcinogenesis that incorporatesan existing understanding of the genomic basis of cancer into afundamentally different paradigm. The findings suggest that cancer“conscripts” the human genome to meet its needs under conditions ofsystemic metabolic stress.

Health and cancer can be seen to reflect underlying IEM-like phenotypicstates that result from variable levels of mitochondrial and peroxisomaldysfunction. These dysfunctions over the course of a normal lifespanmight, or might not, lead to the condition of “metabolic insufficiency”that those recognize as cancer. As one ages, the accumulation of toxicmetabolites, onco-metabolites, DNA and histone methylation tips themfrom the state relative compensation to one of de-compensation asmalignancy arises.

Described herein are blood biomarker panels based upon phenotypicfeatures that are shared by IEM, liver and thyroid dysfunctions andcancers of glandular ancestries.

Using the identified signatures, the inventors explored correlationswith other states of metabolic stress including diabetes mellitus andpolycystic ovary syndrome and showed that they could recapitulate themalignant phenotype in a murine model by exposing hypoglycemic mice toexogenous insulin.

These phenotypic signatures share features of human cellular metabolicstemness and suggest that the same metabolic cascades that sponsorsuccessful embryogenesis, a paradigm of stemness, are shared orre-activated, systemically, during periods of insulin/glucose imbalance.

The described metabolic stresses would, in the majority of thepopulation, be counteracted by the up-regulation of gluconeogenesis andfatty acid oxidation. However, persons manifesting IEM-like phenotypesmay be unable to marshal these critical responses, leading to theaberrant dependence upon MYC-related metabolic reprogramming.

This would reflect an underlying “tendency” to malignant transformationunleashed by stressors, that in breast cancer are “uncovered” byexacerbating risk factors, such as nulliparity, obesity and lifestylebut which only become manifest in those pre-disposed women who carry thefeatures of inborn-priming.

The finding that the metabolic phenotype identified in the blood andtumor tissue of colon cancer patients is identical to the signaturefound in those same patients' normal colonic mucosa supports thehypothesis that cancer arises as a local manifestation of a state asystemic metabolic insufficiency.

Variable levels of metabolic stress, therefore, would be different fromindividual to individual depending on inherited, mild to moderatemetabolic deficiencies, reminiscent of IEM, but not severe enough tocause disease during much of life.

These signatures identify clinical breast cancer irrespective of stage,histology, intrinsic subtype, BMI, menopausal status or age with anaccuracy of 95%, and are also shown to predict tumor response toneoadjuvant chemotherapy and overall survival.

The clinical implications of these findings are several and include thedevelopment of a new diagnostic test for the early detection of breastcancer and its application for prognosis and the prediction of response.The findings may also apply to other cancers of glandular histology.More importantly, the results reflect the application of a phenotypicsignature that can dovetail nicely with advances in genomics,transcriptomics and proteomics as we strive for a more globalunderstanding of human illness.

In conclusion, the invention includes phenotypic evidence supporting thehypothesis that cancers of glandular ancestry, particularly breastcancer, represent the end result of pre-existing metabolic perturbationsassociated with a MYC-induced systemic condition: Cancer as a metabolicepiphenomenon.

Ovary Cancer Signature

Ovary cancer today is recognized as a type of malignancy originated, inthe majority of times, from its surrounding tissues, particularly thefimbria, the very external end of the fallopian tube. The AmericanCancer Society estimates that in the United States, for 2018, there areabout 22,240 new cases, out of which, more than 50% (14,070) of womenwill die from this disease. Ovarian cancer, therefore, is accounting formore deaths than any other cancer of the female reproductive system.This cancer mainly develops in 63 years or older women and it is morecommon in white than African-American women.

Ovarian cancer is difficult to detect, especially in the early stages.This is partly due to the fact that the ovaries—two small, almond-shapedorgans on either side of the uterus—are deep within the abdominalcavity.

Fewer than one-half of women diagnosed with ovarian cancer survivelonger than 5 years, and although the 5-year survival of patients withlocalized ovarian cancer is greater than 90%, only 15% of all women arediagnosed with localized disease.

Currently, no organization recommends screening average-risk women forovarian cancer. Nevertheless, screening and diagnostic methods forovarian cancer include pelvic examination, cancer antigen 125 (CA 125)as a tumor marker, transvaginal ultrasound (TVU), and potentiallymultimarker panels and bioinformatic analysis of proteomic patterns.

However, the performance of these tests for screening when used alone orin combination has been poor. The sensitivity and specificity of pelvicexamination for the detection of asymptomatic ovarian cancer are poorand do not support physical examination as a screening method. CA 125has limited sensitivity and specificity, as does TVU when usedindependently or in combination.

In 2011, the Prostate Lung Colorectal and Ovarian (PLCO) initiativeconcluded, with regards of ovarian cancer screening, that there wasadequate evidence that annual screening with CA 125 and TVU does notreduce ovarian cancer mortality, and that, there was adequate evidencethat screening for ovarian cancer can lead to important harms, mainlysurgical interventions in women without ovarian cancer.

Therefore, an urgent need exists in the art for new and highly sensitivescreening procedures, preferably less demanding without the need ofseveral specialized equipment and personnel or resources.

In view of the above-mentioned problems existing in the art, the objectunderlying the present invention is the provision of new biomarkers forassessing ovary cancer, which allows for screening of ovary cancer in anearly stage of disease progression with high accuracy and reliability.

Optimally, the marker should be easily detectable in a biological samplesuch as in blood and its level should be consistently related to thestage of ovary cancer. Moreover, it is an object of the presentinvention to provide for a method for assessing ovary cancer in abiological sample, which allows for fast, convenient and high throughputperformance.

In order to solve the objects underlying the present invention theinventors based their investigations on metabolomics as it could giveinsight in the biochemical changes occurring in the course of ovarycancer development and offer several novel and potentially betterbiomarkers.

The invention is an early-diagnosis-tool that identifies patients withovarian cancer in its earliest stages, when intervention offers thehighest possibility of cure. The invention provides prognosticinformation and serves as a predictive test for clinical response andsurvival.

The inventors found that a more comprehensive picture of allmetabolomics pathways and mechanisms involved in ovary cancer is givenwhen using a panel of metabolites that are altered in parallel of cancerrather than employing the screening techniques performed in the art,such as ultrasound.

Therefore, the present invention provides for never described biomarkers(i.e. a new biomarker set) suitable for assessing ovary cancer,including early and more advanced stages of disease and also providesbiomarker sets that clearly discriminate, at baseline, patients withelevated risk of relapse after initial treatment.

Moreover, the present invention also provides for a method for assessingovary cancer in a mammalian subject that was achieved and developedtaking into consideration comprehensive and extensive comparisons notonly with several other malignancies but also with several metabolicbenign conditions and, therefore, can be considered as the closest stageof an ideal tumor marker.

In particular, the application of targeted quantitative massspectrometry (MS/MS) to the blood of ovarian cancer patients led to thecreation of a metabolic signature that provides clinically validateddiagnostic and prognostic information for women with ovarian cancer andthose at risk for the disease.

Targeted, quantitative MS/MS provides annotated blood concentrations ofmetabolites that are essential for the accurate determination ofclinically relevant metabolic signatures. Individual metaboliteconcentrations and qualitative, non-targeted, measures do not providethe necessary rigor that is required for the accurate identification ofcancer-related metabolic perturbations.

In a first embodiment, the biomarkers and biomarker sets of the presentinvention are used for screening of subjects, such as human patients,potentially suffering from ovary cancer and diagnosis of ovary cancer inthese subjects.

It has surprisingly been found in the present invention that thebiomarkers and biomarker sets as described herein are particularlyuseful for fast, easy and high throughput screening of a large number ofsubjects, such as human patients, and for diagnosis of ovary cancer fromblood samples of these subjects with improved accuracy of results.

Although accuracy and reliability of screening and/or diagnosis, asdetermined by the parameters of one or more of specificity, sensitivity,PPV and NPV, by using the above-specified biomarker combination isalready greatly improved compared with the prior art techniques, such asultrasound, the accuracy and reliability can be further improved byusing one or more, preferably two or more, further preferably three ormore additional metabolites.

Hence, in a preferred embodiment the biomarker set further comprises oneor more additional amino acid, such as those included in FIG. 2. Theadditional amino acids are preferably selected from glucogenic/ketogenicamino acids such as glycine, cysteine, alanine, arginine, proline,aspartate, asparagine, methionine, isoleucine, leucine, lysine,threonine phenylalanine, tyrosine and tryptophan, most preferablyasparagine and aspartate.

Moreover, the lipid is preferably selected from sphingolipids andglycerolipids, such as glycerophospholipids, e.g. one or more of thelipids included in FIGS. 4-6.

Further preferably, the lipid is derived from arachidonic acid,preferably arachidonic acid derived lipids containing 36 or more carbonatoms, and most preferably is selected from arachidonic polyunsaturatedphosphatidylcholine acyl-alkyl or acyl-acyl, arachidonicmono-unsaturated phosphatidylcholine acyl-alkyl or acyl-acyl andarachidonic saturated phosphatidylcholine acyl-alkyl or acyl-acyl.

In a further preferred embodiment, the combination of metabolitesfurther comprises one or more of lipids described in FIGS. 4-6 and oneor more acylcarnitines as well as carnitine (C0) described in FIG. 3.

As the method of this embodiment can be performed from blood samples,the method greatly increases the subject's compliance compared to priorart screening techniques, such as ultrasound. In particular, the methodgreatly increases reliability and sensitivity of the screening results,in particular reduces the number of false positive and false negativeresults, and is less time consuming, and thus can be performed with ahigh number of patients.

This can be seen, for example, in FIGS. 12A and B, showing that thesignatures developed for assessing ovarian cancer (i.e., one embodimentof the present invention) have a sensitivity of 98.46%, a specificity of96.62%, and a negative predictive value of 99.90%. In particular, FIG.12A shows a multivariate ROC curve analysis for ovary cancer patients(n-64) compared to healthy participants as well as other malignant andnon-malignant conditions (n=1001). FIG. 12B depicts the performance ofthe identified metabolites and ratios for ovary cancer patients. Thenear 100% negative predictive value (99.90%) makes the present testhighly indicative as a powerful screening tool.

FIG. 13 shows an Ortho-PLSDA Score's plot of ovary cancer patients(n=64) compared to healthy participants as well as other malignant andnon-malignant conditions (n=1001). By processing (e.g., isolating,quantifying, normalizing, etc.) each sample (e.g., blood sample), andthen plotting the initial results (e.g., using an Ortho-PLSDA Score'splot) based on at least one ovarian cancer signature (as identified bythe inventors), each patient clearly falls within (a) the control groupor (b) the ovarian cancer group.

Moreover, portions of the signature provide details on each patient'sprognosis. This can be seen, for example, in FIGS. 14A-D, where variousequations (identified at the top of each chart) provide survival rate(prognosis) information for each patient. Thus, not only have theinventors identified signatures that can be used to diagnosis ovariancancer, but also to prognose ovarian cancer. It should be appreciatedthat while the charts provided in FIGS. 13 and 14A-D illustrate (a)diagnosis for ovarian cancer and (b) survival rates, the presentinvention is not so limited, and the ovarian signatures (or portionsthereof) can be used to provide other assessments for ovarian cancer,including screening for, diagnosing, prognosing, treating the same asdiscussed in greater detail in the results section below.

A preferred signature (or portions thereof) for assessing ovarian canceris provided in FIG. 15, including a core ovarian cancer equation,metabolite enhancers, ratio enhancers, and core equations withenhancers. As can be seen in FIG. 15, the core ovarian cancer equationis (C5:1/C5:1-DC), or a ratio of Tiglylcarnitine to Glutaconylcarnitine(see FIG. 3). The inventors have discovered that this ratio ofindividual metabolites, after quantification, normalization, etc., arecritical in assessing a patient for ovarian cancer. Other key portionsinclude [Orn/(AspdC18:1)] (where “d” is divided by, i.e., Asp/C18:1)),[(Orn/Arg)Trp], [C12-DC/(C5:1/C5:1-DC)], and[(C18:1/Asp)/(C5:1/C5:1-DC)], which can be used to not only diagnose,but prognose for ovarian cancer.

While not a limitation of the present invention, targeted metabolomicanalysis of plasma and tissue samples may be performed using theBiocrates Absolute-IDQ P180 (BIOCRATES, Life Science AG, Innsbruck,Austria). This validated targeted assay allows for simultaneousdetection and quantification of metabolites in plasma and tissue samplesin a high-throughput manner.

As discussed in the breast cancer—patients and methodology section,absolute quantification (μmol/L) of blood metabolites may be achieved bytargeted quantitative profiling of 186 annotated metabolites byelectrospray ionization (ESI) tandem mass spectrometry (MS/MS) in aplurality of biological samples. The process described in that sectionis equality applicable here, where a targeted profiling scheme is usedto quantitatively screen for fully annotated metabolites, an xls file isgenerated, which includes sample identification and 186 metabolite namesand concentrations with the unit of μmol/L of plasma, andlog-transformation is applied to all quantified metabolites to normalizethe concentration distributions and processed. ROC curves are thengenerated, and significant feature are used to build classificationmodels.

In total, 186 annotated metabolites were quantified using the p180 kit(BIOCRATES Life Sciences AG, Innsbruck, Austria), including the onesdescribed in the breast cancer—patients and methodology section. Aswell, groups of metabolites related to specific functions were assembledas ratios, and other mathematical relationships were observed (asdiscussed above) (e.g., summing of levels of amino acids, summing totalacylcarnitines, proportions among sums of saturated, monounsaturated andpolyunsaturated structural lipids, etc.). See discussion above in thebreast cancer—patient and methodology section.

With respect to ovarian cancer, samples were injected into a ShimadzuProminence LC system coupled to an AB-Sciex 5600 Triple TOF massspectrometer instrument with an acquisition scan rate of 100 spectra/secand stable mass accuracy of ˜2 ppm. Flow Injection Analysis (FIA) wasperformed using isocratic elution with Methanol/Water (90/10) with 5.0mM of ammonium formate. Flow rate and injection volumes were 0.025mL/min and 50 μL respectively.

No ion source or declustering potential (50 V and −40 V) optimizationwas performed. The following ionization parameters were applied: CUR=20psi, GS1=20 psi, GS2=15 psi, Temp=250° C., IS=5000 V (−4000V). MS scanranging from m/z 100 to 1200 with accumulation time of 0.25 s andproduct ion scan from m/z 100 to 1200 and accumulation time of 0.03 sare the adopted parameters during survey and dependent scansrespectively.

Specific parameters defining the presence of ovarian cancer usingtargeted quantitative MS/MS are provided in FIG. 8. Specific metabolicratios defining presence of ovarian cancer using targeted quantitativeMS/MS are provided in FIG. 9.

When the metabolic profiles of patients with different tumors (lung,colon, liver, leukemias, lymphomas and squamous cells carcinoma of headand neck) were examined, the results demonstrated enhanced glutamineconsumption, particularly in patients harboring tumors of glandularancestries. Extending these studies to include patients with polycysticovary syndrome (PCOS), cirrhosis, high-risk of breast cancer and stage 5metabolic syndrome revealed that these cancer-free participantsmanifested glutaminolytic profiles that were very similar to those foundin adenocarcinoma patients.

The ratio (Glu/Hexoses) was assembled, following the in vitrodemonstration of the “glutamate pulling effect,” where glucosestarvation in malignant cells culture leads to elevations in glutamatethrough a MYC-coordinated reaction. This effect was clearly identifiedin the blood of patients harboring adenocarcinomas, those at higher riskof breast cancer and individuals with PCOS. Noteworthy, neither of thecontrol groups composed of population-based normal controls or patientswith non-glandular tumors (leukemias, lymphomas, multiple myelomas andsquamous cell carcinomas) revealed marked changes in this ratioparticularly squamous cell carcinomas that revealed similar levels tocontrols. Increases in the “glutamate pulling effect” have beendescribed under conditions of metabolic stress induced by glucosedeprivation.

In agreement, the inventors found a significant (p=0.003, FDR=0.009)inverse correlation between patient blood hexoses concentrations and thevalues of our breast cancer equation {[PC aa36:6/[(Val/Phe)/Taurine]/C10:2}. In line with the premise that glandularcancers are promoted under conditions of relative hypoglycemia, measuredas the “glutamate pulling effect,” their results suggest that theisolated determination of blood glucose levels may not be as informativeas the measurement of hexose levels in relation to other metabolicintermediates including: i) the mitochondrial carnitinepalmitoyltransferase II (CPT-2) deficiency ratio (C16/C3), ii) theperoxisomal impairment biomarkers lysoPC a C26:0, lysoPC a C26:1 andlysoPC a C28:1, or iii) its relation to glutaminolysis[Phe/(Gln/Glu)/Asp].

Importantly, both CPT-2 and peroxisomal deficiencies, well known inbornerrors of metabolism, are associated with hypoglycemia in afflictedpatients. If a state of relative hypoglycemia were to occur in ovarycancer as the result of inborn-like errors of metabolism thenhyperinsulinemia associated with chronic hypoglycemia would constitute apowerful metabolic stressor capable of systemically up-regulatingglycolysis and glutaminolysis, even in the absence of cancer.

In sum, carcinogenesis is a complex, polygenic process that draws uponnumerous altered cellular functions leading ultimately, over decades, toa state of irreversible malignant transformation. Molecular signaturesas static measures cannot capture the dynamic nature of biologicalprocesses as they fail to encompass the complexity, redundancy andpromiscuity of these events.

Malignant transformation demands that cells successfully traversemetabolic, structural and immune evasive strategies. This methodologyuses a multi-dimensional invention to define malignant transformation asa metabolic signature.

This invention uses targeted quantitative MS/MS, to define unique andpreviously unknown relationships between bio-energetic, biosynthetic andimmune phenotypes in patients with ovarian cancer. This signaturedefines the ovarian cancer phenotype and is applied to diagnose andprovide prognostic information for patients with ovarian cancer andthose at risk for the development of ovarian cancer.

The invention extends to other malignancies as there are commonalitiesbetween ovarian cancers and other tumor types, and is applicable tourine and saliva, as these body fluids represent additional sources ofmaterial for the assessment of the metabolic signatures defined inblood.

Colorectal Cancer (CRC) Signature

Colorectal cancer is the third most common malignancy diagnosed in bothmen and women in the United States and according to the American CancerSociety estimates, a total of 101,523 CRC new cases are expected for theupcoming year, being 97,220 of colon and 43,030 of rectal cancer.

Overall, the lifetime risk of developing colorectal cancer is: about 1in 22 (4.49%) for men and 1 in 24 (4.15%) for women being the thirdleading cause of cancer-related deaths in the United States.

Currently there are 3 in vitro diagnosis (IVD) tests that are routinelyused for CRC screening, the fecal immunochemical test (FIT), thefecal-based DNA test and the blood-based DNA test (the SEPT9 assay). FITtests, that replaced the old fecal occult blood tests (FOBT), exhibitedsatisfactory sensitivity (79%) and specificity (94%) with low costs andtherefore become the major screening test for CRC at the moment.

The sensitivity of the fecal DNA test appeared to be very high due tocombination of multiple methods while its high cost is an obstaclepreventing the test from broad use. Both sensitivity and specificity forthe SEPT9 test in CRC screening were lower than those of the FIT andfecal DNA test, but it showed high compliance with promising future ifits accuracy can be improved.

Combined tests with multiple markers should be a future direction in CRCscreening, however, some hurdles, such as technical integration,test/interpretation optimization, and high costs, etc, need to beovercome before they can be used in large-scale CRC screening aiming atasymptomatic average-risk population.

CEA and carbohydrate antigen 199 (CA199) are the two most commonserum-based glycoprotein CRC markers, however, they are not appropriatefor CRC screening due to their low sensitivity and the lack of CRCspecificity, especially for early-stage CRC.

For example, CEA test exhibited a sensitivity of 40.9%-51.8% and aspecificity of 85.2%-95% for CRC detection in three studies. Therefore,it is more appropriate to be used in monitoring the CRC recurrence orresponse from patients to surgical or systemic therapy, rather thanscreening. The main drawback of serum glycoprotein markers in CRCscreening is that the sensitivity and specificity of any single markeris not high enough to make it a reliable indicator.

In view of the above-mentioned problems existing in the art, the objectunderlying the present invention is the provision of new biomarkers forassessing colorectal cancer, which allows for screening of colorectalcancer in an early stage of disease progression with high accuracy andreliability.

Optimally, the marker should be easily detectable in a biological samplesuch as in blood and its level should be consistently related to thestage of colorectal cancer. Moreover, it is an object of the presentinvention to provide for a method for assessing colorectal cancer in abiological sample, which allows for fast, convenient and high throughputperformance.

In order to solve the objects underlying the present invention theinventors based their investigations on metabolomics as it could giveinsight in the biochemical changes occurring in the course of colorectalcancer development and offer several novel and potentially betterbiomarkers.

The inventors found that a more comprehensive picture of allmetabolomics pathways and mechanisms involved in colorectal cancer isgiven when using a panel of metabolites that are altered in parallel ofcancer rather than employing the screening techniques performed in theart, such as ultrasound.

Therefore, in one embodiment of the present invention, never describedbiomarkers (i.e. a new biomarker set) are provided suitable forassessing colorectal cancer, including early and more advanced stages ofdisease. Also included are biomarker sets that clearly discriminate, atbaseline, patients with elevated risk of relapse after initialtreatment.

Moreover, the present invention also provides for a method for assessingcolorectal cancer in a mammalian subject that was achieved and developedtaking into consideration comprehensive and extensive comparisons notonly with several other malignancies but also with several metabolicbenign conditions and, therefore, can be considered as the closest stageof an ideal tumor marker.

In a first embodiment, the biomarkers and biomarker sets of the presentinvention are used for screening of subjects, such as human patients,potentially suffering from colorectal cancer and diagnosis of colorectalcancer in these subjects.

It has surprisingly been found in the present invention that thebiomarkers and biomarker sets as described herein are particularlyuseful for fast, easy and high throughput screening of a large number ofsubjects, such as human patients, and for diagnosis of colorectal cancerfrom blood samples of these subjects with improved accuracy of results.

Although accuracy and reliability of screening and/or diagnosis, asdetermined by the parameters of one or more of specificity, sensitivity,PPV and NPV, by using the above-specified biomarker combination isalready greatly improved compared with the prior art techniques, such asultrasound, the accuracy and reliability can be further improved byusing one or more, preferably two or more, further preferably three ormore additional metabolites.

Hence, in a preferred embodiment the biomarker set further comprises oneor more additional amino acid, such as those included in FIG. 2. Theadditional amino acids are preferably selected from glucogenic/ketogenicamino acids such as glycine, cysteine, alanine, arginine, proline,aspartate, asparagine, methionine, isoleucine, leucine, lysine,threonine phenylalanine, tyrosine and tryptophan, most preferablyasparagine and aspartate.

Moreover, the lipid is preferably selected from sphingolipids andglycerolipids, such as glycerophospholipids, e.g. one or more of thelipids included in FIGS. 4-6.

Further preferably, the lipid is derived from arachidonic acid,preferably arachidonic acid derived lipids containing 36 or more carbonatoms, and most preferably is selected from arachidonic polyunsaturatedphosphatidylcholine acyl-alkyl or acyl-acyl, arachidonicmono-unsaturated phosphatidylcholine acyl-alkyl or acyl-acyl andarachidonic saturated phosphatidylcholine acyl-alkyl or acyl-acyl.

In a further preferred embodiment, the combination of metabolitesfurther comprises one or more of lipids described in FIGS. 4-6 and oneor more acylcarnitines as well as carnitine (C0) described in FIG. 3.

As the method of this embodiment can be performed from blood samples,the method greatly increases the subject's compliance compared to priorart screening techniques, such as ultrasound. In particular, the methodgreatly increases reliability and sensitivity of the screening results,in particular reduces the number of false positive and false negativeresults, and is less time consuming, and thus can be performed with ahigh number of patients.

This can be seen, for example, in FIGS. 16A and B, showing that thesignatures developed for assessing colon cancer (i.e., one embodiment ofthe present invention) have a sensitivity of 98.84%, a specificity of98.40%, and a negative predictive value of 99.88%. In particular, FIG.16A shows a multivariate ROC curve analysis for colon cancer patients(n-85) compared to healthy participants as well as other malignant andnon-malignant conditions (n=800). FIG. 16B depicts the performance ofthe identified metabolites and ratios for colon cancer patients. Thenear 100% negative predictive value (99.88%) makes the present testhighly indicative as a powerful screening tool.

FIG. 17 shows an Ortho-PLSDA Score's plot of colon cancer patients(n=85) compared to healthy participants as well as other malignant andnon-malignant conditions (n=800). By processing (e.g., isolating,quantifying, normalizing, etc.) each sample (e.g., blood sample), andthen plotting the initial results (e.g., using an Ortho-PLSDA Score'splot) based on at least one colon cancer signature (as identified by theinventors), each patient clearly falls within (a) the control group or(b) the colon cancer group.

Moreover, portions of the signature provide details on each patient'sprognosis. This can be seen, for example, in FIGS. 18A-C, where variousequations (identified at the top of each chart) provide survival rate(prognosis) information for each patient. Thus, not only have theinventors identified signatures that can be used to diagnosis coloncancer, but also to prognose colon cancer. It should be appreciated thatwhile the charts provided in FIGS. 17 and 18A-C illustrate (a) diagnosisfor colon cancer and (b) survival rates, the present invention is not solimited, and the colon signatures (or portions thereof) can be used toprovide other assessments for colon cancer, including screening for,diagnosing, prognosing, treating the same as discussed in greater detailin the results section below.

A preferred signature (or portions thereof) for assessing colon canceris provided in FIG. 19, including a core ovarian cancer equation,metabolite enhancers, and core equations with enhancers. As can be seenin FIG. 19, the core ovarian cancer equation is (C16:1/PC aa C34:2), ora ratio of Hexadecenoylcarnitine to Phosphatidylcholine with diacylresidue sum (see FIGS. 3 and 5). The inventors have discovered that thisratio of individual metabolites, after quantification, normalization,etc., are critical in assessing a patient for colon cancer. Other keyportions include {SM C20:2/[(C16:1/PC aa C34:2)/C5:1-DC]}, {SM OHC16:1/[(C16:1/PC aa C34:2)/C5:1-DC]}, and {SM OH C14:1/[(C16:1/PC aaC34:2)/C5:1-DC]}, which can be used to not only diagnose, but prognosefor colon cancer.

Pancreatic Cancer Signature

Pancreatic cancer arises when cells in the pancreas, a glandular organbehind the stomach, begin to multiply out of control and form a mass.These cancerous cells can invade other parts of the body. There areusually no symptoms in the disease's early stages, and symptoms that arespecific enough to suggest pancreatic cancer typically do not developuntil the disease has reached an advanced stage. By the time ofdiagnosis, pancreatic cancer has often spread to other parts of thebody.

In 2015, pancreatic cancers of all types resulted in 411,600 deathsglobally. Pancreatic cancer is the fifth most common cause of death fromcancer in the United Kingdom, and the third most common in the UnitedStates. The disease occurs most often in the developed world, whereabout 70% of the new cases in 2012 originated. Pancreatic adenocarcinomatypically has a very poor prognosis: after diagnosis, 25% of peoplesurvive one year and 5% live for five years. For cancers diagnosedearly, the five-year survival rate rises to about 20%.

Pancreatic cancer is usually diagnosed by a combination of medicalimaging techniques such as ultrasound or computed tomography, bloodtests, and examination of tissue samples (biopsy). The disease isdivided into stages, from early (stage I) to late (stage IV). Screeningthe general population has not been found to be effective.

In view of the above-mentioned problems existing in the art, the objectunderlying the present invention is the provision of new biomarkers forassessing pancreatic cancer, which allows for screening of pancreaticcancer in an early stage of disease progression with high accuracy andreliability.

Optimally, the marker should be easily detectable in a biological samplesuch as in blood and its level should be consistently related to thestage of pancreatic cancer. Moreover, it is an object of the presentinvention to provide for a method for assessing pancreatic cancer in abiological sample, which allows for fast, convenient and high throughputperformance.

In order to solve the objects underlying the present invention theinventors based their investigations on metabolomics as it could giveinsight in the biochemical changes occurring in the course of pancreaticcancer development and offer several novel and potentially betterbiomarkers.

The inventors found that a more comprehensive picture of allmetabolomics pathways and mechanisms involved in pancreatic cancer isgiven when using a panel of metabolites that are altered in parallel ofcancer rather than employing the screening techniques performed in theart, such as ultrasound or computed tomography.

Therefore, in one embodiment of the present invention, never describedbiomarkers (i.e. a new biomarker set) are provided suitable forassessing pancreatic cancer, including early and more advanced stages ofdisease. Also included are biomarker sets that clearly discriminate, atbaseline, patients with elevated risk of relapse after initialtreatment.

Moreover, the present invention also provides for a method for assessingpancreatic cancer in a mammalian subject that was achieved and developedtaking into consideration comprehensive and extensive comparisons notonly with several other malignancies but also with several metabolicbenign conditions and, therefore, can be considered as the closest stageof an ideal tumor marker.

In a first embodiment, the biomarkers and biomarker sets of the presentinvention are used for screening of subjects, such as human patients,potentially suffering from pancreatic cancer and diagnosis of pancreaticcancer in these subjects.

It has surprisingly been found in the present invention that thebiomarkers and biomarker sets as described herein are particularlyuseful for fast, easy and high throughput screening of a large number ofsubjects, such as human patients, and for diagnosis of pancreatic cancerfrom blood samples of these subjects with improved accuracy of results.

Although accuracy and reliability of screening and/or diagnosis, asdetermined by the parameters of one or more of specificity, sensitivity,PPV and NPV, by using the above-specified biomarker combination isalready greatly improved compared with the prior art techniques, such asultrasound, the accuracy and reliability can be further improved byusing one or more, preferably two or more, further preferably three ormore additional metabolites.

Hence, in a preferred embodiment the biomarker set further comprises oneor more additional amino acid, such as those included in FIG. 2. Theadditional amino acids are preferably selected from glucogenic/ketogenicamino acids such as glycine, cysteine, alanine, arginine, proline,aspartate, asparagine, methionine, isoleucine, leucine, lysine,threonine phenylalanine, tyrosine and tryptophan, most preferablyasparagine and aspartate.

Moreover, the lipid is preferably selected from sphingolipids andglycerolipids, such as glycerophospholipids, e.g. one or more of thelipids included in FIGS. 4-6.

Further preferably, the lipid is derived from arachidonic acid,preferably arachidonic acid derived lipids containing 36 or more carbonatoms, and most preferably is selected from arachidonic polyunsaturatedphosphatidylcholine acyl-alkyl or acyl-acyl, arachidonicmono-unsaturated phosphatidylcholine acyl-alkyl or acyl-acyl andarachidonic saturated phosphatidylcholine acyl-alkyl or acyl-acyl.

In a further preferred embodiment, the combination of metabolitesfurther comprises one or more of lipids described in FIGS. 4-6 and oneor more acylcarnitines as well as carnitine (C0) described in FIG. 3.

As the method of this embodiment can be performed from blood samples,the method greatly increases the subject's compliance compared to priorart screening techniques. In particular, the method greatly increasesreliability and sensitivity of the screening results, in particularreduces the number of false positive and false negative results, and isless time consuming, and thus can be performed with a high number ofpatients.

This can be seen, for example, in FIGS. 20A and B, showing that thesignatures developed for assessing pancreatic cancer (i.e., oneembodiment of the present invention) have a sensitivity of 100%, aspecificity of 97.93%, and a negative predictive value of 100%. Inparticular, FIG. 20A shows a multivariate ROC curve analysis forpancreatic cancer patients (n-10) compared to healthy participants aswell as other malignant and non-malignant conditions (n=709). FIG. 20Bdepicts the performance of the identified metabolites and ratios forpancreatic cancer patients. The 100% negative predictive value makes thepresent test highly indicative as a powerful screening tool.

FIG. 21 shows an Ortho-PLSDA Score's plot of pancreatic cancer patients(n=10) compared to healthy participants as well as other malignant andnon-malignant conditions (n=709). By processing (e.g., isolating,quantifying, normalizing, etc.) each sample (e.g., blood sample), andthen plotting the initial results (e.g., using an Ortho-PLSDA Score'splot) based on at least one pancreatic cancer signature (as identifiedby the inventors), each patient clearly falls within (a) the controlgroup or (b) the pancreatic cancer group.

Moreover, portions of the signature provide details on each patient'sprognosis. This can be seen, for example, in FIGS. 22A and B, wherevarious equations (identified at the top of each chart) provide survivalrate (prognosis) information for each patient. For example, FIG. 22Adistinguishes short survival terms (e.g., 6 months) and longer survivalterms (e.g., 15 months). FIG. 22B also distinguishes between short andlong survival terms, but further validates that these findings were ableto prove that the metabolic equation is fully functional in survivalprediction even in malignancies of different origins, such as MultipleMyeloma (M.M.), Leukemias, Lymphomas and Myelodisplasias (where ISS 1,2, and 3=Int'l Scaling System, Hem=Hematological Malignancies, andPanc=Pancreas Cancer). Thus, not only have the inventors identifiedsignatures that can be used to diagnosis pancreatic cancer, but also toprognose pancreatic cancer.

It should be appreciated that while the charts provided in FIGS. 21 and22A-B illustrate (a) diagnosis for pancreatic cancer and (b) survivalrates, the present invention is not so limited, and the pancreaticsignatures (or portions thereof) can be used to provide otherassessments for pancreatic cancer, including screening for, diagnosing,prognosing, treating the same as discussed in greater detail in theresults section below.

A preferred signature (or portions thereof) for assessing pancreaticcancer is provided in FIG. 23, including a core pancreatic cancerequation, metabolite enhancers, and core equations with enhancers. Ascan be seen in FIG. 23, two core pancreatic cancer equations are (1)(C3:1/C12-DC), or a ratio of Propenoylcarnitine toDodecanedioylcarnitine, and (2) (C6:1/C12-DC), or a ratio ofHexenoylcarnitine to Dodecanedioylcarnitine. See FIG. 3. The inventorshave discovered that this ratio of individual metabolites, afterquantification, normalization, etc., are critical in assessing a patientfor pancreatic cancer. Other key portions include (C:12-DC/lysoPC aC17:0) and (C12-DC/lysoPC a C17:0), which can be used to not onlydiagnose, but prognose for pancreatic cancer.

Acute Graft Versus Host Disease (AGVHD) and Risk of AllogeneicHematopoietic Stem Cell Transplantation (AHSCT) Signature

Allogeneic hematopoietic stem cell transplantation (AHSCT) exemplifiesthe usage of an effective therapeutic strategy for a variety ofhematological malignancies so that the flawlessness of the technique hasnowadays lengthened its practice. Nevertheless, the technique is notfree of any problem.

Indeed immunological-arbitrated difficulties, such as acute (AGVHD) andchronic graft-versus-host disease (CGVHD), usually observed in more than50% of patients submitted to AHSCT remain a very important limitingfactor in survival.

As a result, the indication for AHSCT should be more individualized andbased on the expected long-term disease-free survival with conventionalchemotherapy versus the risk of relapse and the risk of treatmentrelated mortality/morbidity after transplantation.

Some strategies based on pre-transplantation prognostic factors areassociated with long-term survival nevertheless; none of the availableclinical and/or biochemical tools are capable to accurately predict theoccurrence of AGVHD.

In view of the above-mentioned problems existing in the art, the objectunderlying the present invention is the provision of new biomarkers forassessing, prior to starting the allogeneic hematopoietic stem celltransplantation (AHSCT) procedures, the patients at increased risk todevelop AGVHD.

Optimally, the marker should be easily detectable in a biological samplesuch as in blood and its level should be consistently related to thestage of hematological cancer. Moreover, it is an object of the presentinvention to provide for a method for assessing hematological cancer ina biological sample, which allows for fast, convenient and highthroughput performance.

In order to solve the objects underlying the present invention theinventors based their investigations on metabolomics as it could giveinsight in the biochemical changes occurring in the course ofhematological cancer development and offer several novel and potentiallybetter biomarkers.

The inventors found that a more comprehensive picture of allmetabolomics pathways and mechanisms involved in hematologicalmalignancies is given when using a panel of metabolites that are alteredin parallel of cancer behavior.

Therefore, in one embodiment of the present invention, new biomarkers(i.e. a new biomarker set) suitable for assessing, at baseline, the riskto develop AGVHD after allogeneic hematopoietic stem celltransplantation (AHSCT) transplant are provided.

Moreover, the present invention also provides for a method for assessinghematological cancer in a mammalian subject on the basis of thebiomarkers and biomarker sets as described herein.

It has surprisingly been found in the present invention that thebiomarkers and biomarker sets as described herein are particularlyuseful for fast, easy and high throughput screening of a large number ofsubjects, such as human patients, and for diagnosis of hematologicalcancer from blood samples of these subjects with improved accuracy ofresults.

Although accuracy and reliability of screening and/or diagnosis, asdetermined by the parameters of one or more of specificity, sensitivity,PPV and NPV, by using the above-specified biomarker combination isalready greatly improved compared with the prior art techniques, such asultrasound, the accuracy and reliability can be further improved byusing one or more, preferably two or more, further preferably three ormore additional metabolites.

Hence, in a preferred embodiment the biomarker set further comprises oneor more additional amino acid, such as those included in FIG. 2. Theadditional amino acids are preferably selected from glucogenic/ketogenicamino acids such as glycine, cysteine, alanine, arginine, proline,aspartate, asparagine, methionine, isoleucine, leucine, lysine,threonine phenylalanine, tyrosine and tryptophan, most preferablyasparagine and aspartate.

Moreover, the lipid is preferably selected from sphingolipids andglycerolipids, such as glycerophospholipids, e.g. one or more of thelipids included in FIGS. 4-6.

Further preferably, the lipid is derived from arachidonic acid,preferably arachidonic acid derived lipids containing 36 or more carbonatoms, and most preferably is selected from arachidonic polyunsaturatedphosphatidylcholine acyl-alkyl or acyl-acyl, arachidonicmono-unsaturated phosphatidylcholine acyl-alkyl or acyl-acyl andarachidonic saturated phosphatidylcholine acyl-alkyl or acyl-acyl.

In a further preferred embodiment, the combination of metabolitesfurther comprises one or more of lipids described in FIGS. 4-6 and oneor more acylcarnitines as well as carnitine (C0) described in FIG. 3.

As the method of this embodiment can be performed from blood samples,the method greatly increases the subject's compliance compared to priorart screening techniques, such as ultrasound. In particular, the methodgreatly increases reliability and sensitivity of the screening results,in particular reduces the number of false positive and false negativeresults, and is less time consuming, and thus can be performed with ahigh number of patients.

Determining and Providing Results

The invention may involve a patient visiting a doctor, clinician,technician, nurse, etc., where blood or a different sample is collected.The sample would then be provided to a laboratory for analysis, asdiscussed above (e.g., mass spectrometry, log-transformation,comparisons, etc.). In another embodiment, a kit can be used to obtainthe sample, where the kit is made available to the patient via a medicalfacility, a drug store, the Internet, etc. In this embodiment, the kitmay include one or more wells and one or more inserts impregnated withat least one internal standard. The kit can be used to gather the samplefrom a patient, where the sample is then provided to a laboratory foranalysis.

For example, as shown in FIG. 1, peripheral blood may collected intoEDTA-anticoagulant tubes. Plasma is isolated by centrifugation. Plasmasamples may then be submitted to a p180 AbsolutelDQ kit for extractionand processing. In one embodiment, prepared samples will then undergoliquid chromatography (LC) followed by Flow Injection Analysis (FIA) bytandem Mass Spectrometry (MS/MS) (i.e., metabolite extraction). Theextracted data is then processed using computer software. For example,the data acquired may then be normalized (e.g., via log-transformation)and stored in a database that includes at least (i) patientidentification, (ii) metabolite name, and (iii) quantification. If thisdata is on known individuals (individuals with known conditions), thenit can be analyzed to determine signatures that can be used to assess aparticular disease. If, however, the data is on a patient whosecondition is unknown, then it can be compared to known signatures (e.g.,stored in memory) to screen for, diagnose, prognose, and treat thepatient.

It should be appreciated that the present invention is not limited tonormalizing a quantified metabolite. In other words, other processesdiscussed herein and/or generally known to those skilled in the art maybe performed either before or after normalization. It should also beappreciated that while certain processes can be performed manually, most(if not all) should preferably be performed using software, whereinitial results (data post mass spectrometry, post normalization), arestored in memory, presented on a display (e.g., computer monitor, etc.)and/or printed. The initial results can then be compared to known“signatures” for different diseases, where similarities and differencesare used to screen for, diagnose, prognose, treat, etc. a particulardisease. It should be appreciated that the sample may be assessed for aparticular disease, or for multiple diseases, depending on the patient'ssex, age, etc. Thus, the software could be used to assess a particulardisease or assess at least one disease from a plurality of diseases.

It should further be appreciated that the “comparing” step can beperformed by (i) software, (ii) a human, or (iii) both. For example,with respect to the prior, a computer program could be used to comparesample results to known signatures and to use differences and/orsimilarities thereof to assess at least one disease, and providediagnosis, prognosis, and/or treatment for the same. Alternatively, inthe second embodiment, a technician could be used to compares sampleresults to known signatures (or aspects thereof) and make a diagnosis,prognosis, and/or treatment decision based on perceived similaritiesand/or differences. Finally, with respect to the latter, a computerprogram could be used to plot (e.g., on a computer display) sampleresults alongside known signatures (e.g., signatures of healthypatients, signatures of unhealthy patients, life expectancies, etc.). Atechnician could then view the same and make at least one diagnosis,prognosis, treatment recommendation, etc. based on similarities and/ordifferences in the plotted information.

Bottom line, it is the differences and/or similarities between knownsignatures that allows a disease to be assessed, whether that assessmentis automated (e.g., performed by a computer), performed manually (e.g.,done by a human), or a combination of the two.

Results (e.g., assessments) are then provided to the patient directly(e.g., via mail, an electronic communication, etc.) or via the patient'sdoctor, and can include screening information, diagnosis information,prognosis information, and treatment information.

In particular, the invention can be used to distinguish a sample that iscancerous from one that is normal. If it is cancerous, then theinvention can further be used to distinguish, breast from ovary, ovaryfrom lung, lung from colon, etc. Once the cancer is identified (e.g.,ovarian, breast, etc.), the invention can be used to define the cancer,by degree, the relative malignancy of the cancer. This can be done usingterminology (e.g., non-invasive (e.g., in situ), invasive, metastatic,and lethal), at least one scale (e.g., 1-10, 1-100, A-F, etc.), whereone end of the scale is low grade (e.g., non-invasive) and the other endis high grade (lethal), or other visual forms (e.g., color coded, 2D or3D model, etc.).

The invention can also be used to provide a prognosis. For example, inovarian cancer, once the ovarian signature is identified, the inventioncan be used to provide gradations within the signature (or signatures),subcategorizing the patient into one that is likely to survive (e.g.,greater than 3 years, 5 years, 10 years, etc.), likely to relapse (e.g.,within 3 years, 5 years, 10 years, etc.), or likely to die (e.g., within3 years, 5 years, 10 years, etc.). Again, prognosis could be providedusing terminology (e.g., low risk, medium risk, high risk, etc.), atleast one scale, or other visual forms.

Not only can the present invention be used to determine life expectancyand remission rate, it can also be used to determine treatment, orviability of treatment (another form of prognosis). This could be alikelihood to respond to therapy (e.g., hormonal, radiation,chemotherapy, etc.), which again could be provided using terminology, atleast one scale, or other visual forms.

Thus, by way of example, the present invention may be used to determine(i) a high likelihood that a patient harbors a cancer (diagnosis), (ii)a high likelihood that the cancer is ovarian (diagnosis), (iii) likelydrug resistant (prognosis), (iv) high risk of relapse (prognosis), and(v) high risk of death within 3-5 years (prognosis). Clearly this isexemplary, and other diseases (e.g., breast, colon, ovarian, etc.),sub-categorizations (e.g., indolent, aggressive, very aggressive, etc.),prognosis (e.g., reoccurrence within 3 years, 5 years, 10 years, etc.),and treatments (e.g., resistant to hormonal therapy, chemotherapy,radiation therapy, etc.) can be identified (predicted) using the presentinvention.

The invention can also be used to screen for diseases. Medical screeningis the systematic application of a test or inquiry to identifyindividuals at sufficient risk of a specific disorder to benefit fromfurther investigation or direct preventative action (these individualsnot having sought medical attention on account of symptoms of thatdisorder). The present invention uses metabolic signatures to screen fordiseases in populations who are considered at risk. For ovarian cancer,this may be woman in their 40s or 50s with a family history, or otherrisk factors.

It should be appreciated that while several examples have been providedas to what the present invention can discern from a blood sample (or thelike), the present invention is not so limited, and other types ofdiagnosis and prognosis, including treatments, are within the spirit andscope of the present invention. For example, breast cancer may beidentified as ductal, tubular, medullary, mucinous, papillary,cribriform, lobular, etc. It may also be identified by its prognosis(e.g., triple negative, etc.). Those skilled in the art will understandthat similar classifications can be provided for other cancers, wheresuch classification are generally known to those skilled in the art. Allsuch classifications, for both diagnosis and prognosis, are within thespirit and scope of the present invention.

As shown in FIG. 1, once a sample has been received and processed (e.g.,processed using techniques like the one used to identify the signaturesin the first place, such as mass spectrometry (to quantify metabolites),log-transformation (or other mathematical manipulation to normalize thedata), etc.), the initial results (e.g., metabolites and/or setsthereof) can then be compared to signatures (or portions thereof) thathave been identified (by the inventors) as useful in assessing at leastone disease. The signatures may be stored in memory, and the initialdata (i.e., processed sample) may be compared to at least one signatureeither manually (e.g., by viewing the sample, or initial resultsthereof, against known signatures), automatically (e.g., using acomputer program to discern differences and/or similarities between thesample, or initial results thereof, and known signatures), or both(e.g., a program determines at least one diagnosis/prognosis and atechnician reviews the data to validate the same). Based on the results(i.e., comparison results), at least one diagnosis and/or prognosis,which may or may not include treatment, is identified and provided tothe patient.

Conclusion

Having thus described several embodiments of a system and method forusing new biomarkers for assessing different diseases, it should beapparent to those skilled in the art that certain advantages of thesystem and method have been achieved. It should also be appreciated thatvarious modifications, adaptations, and alternative embodiments thereofmay be made within the scope and spirit of the present invention. Theinvention is solely defined by the following claims.

What is claimed is:
 1. A method for assessing a human patient forovarian cancer, comprising: using a technology selected fromchromatography, spectroscopy, and spectrometry to quantify a pluralityof metabolites included in a blood sample obtained from said humanpatient, including at least Tiglycarnitine and Glutaconylcarnitine;normalizing at least said Tiglycarnitine and said Glutaconylcarnitine,as quantified using said technology; comparing at least a result of anequation comprising at least a first ratio of said Tiglylcarnitine tosaid Glutaconylcarnitine, as normalized, to at least one predeterminedvalue to both diagnose said human patient for said ovarian cancer anddetermine a prognosis for said human patient; wherein said diagnosisincludes at least whether said human patient has ovarian cancer and saidprognosis includes at least a risk factor associated with said ovariancancer.
 2. The method of claim 1, further comprising the steps ofquantifying and normalizing Dodecanedioylcarnitine, wherein saidequation further comprises at least a second ratio of saidDodecanedioylcarnitine, as quantified and normalized, to said firstratio.
 3. The method of claim 1, further comprising the steps ofquantifying and normalizing Octadecenoylcarnitine and Aspartate, wheresaid equation further comprises at least a second ratio of saidOctadecenoylcarnitine to said Aspartate, as quantified and normalized.4. The method of claim 3, wherein said equation further comprises atleast a third ratio comprising at least said second ratio to said firstratio.
 5. The method of claim 1, further comprising the steps ofquantifying and normalizing Ornithine, Arginine, and Tryptophan, and thestep of comparing at least a second result of a second equationcomprising said Ornithine, said Arginine, and said Tryptophan, asquantified and normalized to at least one other predetermined value toat least determine said prognosis for said human patient.
 6. The methodof claim 1, further comprising the steps of quantifying and normalizingOrnithine, Aspartate, Octadecenoylcarnitine, and comparing at least asecond result of a second equation comprising said Ornithine, saidAspartate, and said Octadecenoylcarnitine to at least one otherpredetermined value to at least determine said prognosis for said humanpatient.
 7. The method of claim 1, wherein said step of normalizing atleast said Tiglycarnitine and said Glutaconylcarnitine further comprisesusing at least a log-transformation to normalize at least saidTiglycarnitine and said Glutaconylcarnitine.
 8. The method of claim 1,wherein said risk factor comprises at least a survival rate of saidhuman patient from said ovarian cancer.
 9. The method of claim 1,wherein said risk factor comprises at least a relapse rate of saidovarian cancer.
 10. The method of claim 1, wherein said step ofcomparing is further used to determine a degree of said ovarian cancer,said determined degree being one of non-invasive, invasive, metastatic,and lethal.
 11. The method of claim 1, wherein said step of comparing isfurther used to determine a viability of at least one treatment for saidovarian cancer.
 12. A system for assessing a human patient for ovariancancer, comprising: a computing system comprising at least one memorydevice for storing machine readable instructions adapted to perform thesteps of: receive a plurality of quantified metabolites from a sampleprovided by said human patient, including at least Tiglycarnitine andGlutaconylcarnitine; normalize said plurality of quantified metabolites;compare at least a result of an equation comprising at least a firstratio of said Tiglylcarnitine to said Glutaconylcarnitine, asnormalized, to at least one predetermined value to determine at leastone level of similarity therebetween; and use said at least one level ofsimilarity to determine a diagnosis and a prognosis for said humanpatient regarding said ovarian cancer; wherein said diagnosis includesat least whether said human patient has ovarian cancer and saidprognosis includes at least a risk factor associated with said ovariancancer.
 13. The system of claim 12, wherein said quantified metabolitesfurther include Dodecanedioylcarnitine, and said equation furthercomprises at least a second ratio of said Dodecanedioylcarnitine to saidfirst ratio.
 14. The system of claim 12, wherein said quantifiedmetabolites further include Octadecenoylcarnitine and Aspartate, andsaid equation further comprises at least a second ratio of saidOctadecenoylcarnitine to said Aspartate.
 15. The system of claim 14,wherein said equation further comprises at least a third ratiocomprising at least said second ratio to said first ratio.
 16. Thesystem of claim 12, wherein said quantified metabolites further includeOrnithine, Arginine, and Tryptophan, and said machine readableinstructions are further adapted to compare at least a second result ofa second equation comprising at least said Ornithine, said Arginine, andsaid Tryptophan to at least one other predetermined value to determine alevel of similarity therebetween, said level of similarity being used atleast to determine said prognosis for said human patient.
 17. The systemof claim 12, wherein said quantified metabolites further includeOrnithine, Aspartate, and Octadecenoylcarnitine, and said machinereadable instructions are further adapted to compare at least a secondresult of a second equation comprising at least said Ornithine, saidAspartate, and said Octadecenoylcarnitine to at least one otherpredetermined value to determine a level of similarity therebetween,said level of similarity being used to at least determine said prognosisfor said human patient.
 18. The system of claim 12, wherein said machinereadable instructions are further adapted to use a log-transformation tonormalize said quantified metabolites.
 19. The system of claim 12,wherein said risk factor comprises at least a survival rate of saidhuman patient from said ovarian cancer.
 20. The system of claim 12,wherein said machine readable instructions are further adapted to usesaid level of similarity therebetween to determine a viability of atleast one treatment for said ovarian cancer.